diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000000000000000000000000000000000000..301e9359ee344ba7331a0ae6566211b717e22feb --- /dev/null +++ b/.gitignore @@ -0,0 +1,6 @@ +__pycache__/ +*.pyc +node_modules/ +.env +*.egg-info/ +dist/ diff --git a/Dockerfile b/Dockerfile new file mode 100644 index 0000000000000000000000000000000000000000..730d7d467651f4395173a60f3606d0399bf08525 --- /dev/null +++ b/Dockerfile @@ -0,0 +1,43 @@ +# SentinelEdge Demo - Hugging Face Spaces Deployment +# Serves React frontend + FastAPI backend from a single container + +FROM node:20-slim AS frontend-build + +WORKDIR /build +COPY demo/frontend/package.json demo/frontend/package-lock.json* ./ +RUN npm install --production=false +COPY demo/frontend/ . +RUN npm run build + +# --- Python runtime --- +FROM python:3.11-slim + +# System deps for PyNaCl (libsodium) +RUN apt-get update && \ + apt-get install -y --no-install-recommends libsodium-dev && \ + rm -rf /var/lib/apt/lists/* + +WORKDIR /app + +# Install only the deps we actually need for the demo (no whisper/pyaudio/onnx) +COPY requirements-deploy.txt . +RUN pip install --no-cache-dir -r requirements-deploy.txt + +# Copy project code +COPY sentinel_edge/ sentinel_edge/ +COPY hub/ hub/ +COPY demo/backend/ demo/backend/ +COPY models/ models/ +COPY federated/ federated/ + +# Copy built frontend into a static dir the backend will serve +COPY --from=frontend-build /build/dist /app/static + +# Copy the deployment server entrypoint +COPY deploy_server.py . + +# HF Spaces expects port 7860 +ENV PORT=7860 +EXPOSE 7860 + +CMD ["python", "deploy_server.py"] diff --git a/README.md b/README.md index 6bf7d55057100ac88ea5f6ac3d9d27eec026046c..e6e1f4072c65a4168dc84f912d562953443056db 100644 --- a/README.md +++ b/README.md @@ -1,12 +1,15 @@ --- title: SentinelEdge -emoji: 📊 -colorFrom: red -colorTo: gray +emoji: 🛡️ +colorFrom: green +colorTo: blue sdk: docker +app_port: 7860 pinned: false license: mit short_description: Federated Edge AI for Real-Time Phone Call Fraud Detection --- -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference +# SentinelEdge Demo + +Real-time phone call fraud detection powered by federated edge AI. Try the interactive demo to see how SentinelEdge analyzes phone calls in real-time and detects scam patterns. diff --git a/demo/backend/audio_streamer.py b/demo/backend/audio_streamer.py new file mode 100644 index 0000000000000000000000000000000000000000..82a508a2162d68bbdc67b5d0e752b758fee21b0a --- /dev/null +++ b/demo/backend/audio_streamer.py @@ -0,0 +1,185 @@ +"""Stream audio files through the detection pipeline. + +Reads WAV files and yields fixed-duration chunks of audio samples, +simulating real-time microphone input for the fraud-detection pipeline. +""" + +from __future__ import annotations + +import io +import wave +from pathlib import Path + +import numpy as np + + +class AudioStreamer: + """Reads a WAV file and streams chunks to simulate real-time audio. + + Parameters + ---------- + sample_rate : int + Expected sample rate of the input WAV files (default 16 kHz). + """ + + def __init__(self, sample_rate: int = 16_000) -> None: + self.sample_rate = sample_rate + + # ------------------------------------------------------------------ + # Public API + # ------------------------------------------------------------------ + + def stream_file( + self, wav_path: str, chunk_duration: float = 1.0 + ): + """Generator that yields audio chunks from a WAV file. + + Each chunk is *chunk_duration* seconds of float32 audio normalised + to the range ``[-1, 1]``. The final chunk may be shorter than the + requested duration. + + Parameters + ---------- + wav_path : str + Path to a 16-bit PCM WAV file. + chunk_duration : float + Duration of each chunk in seconds. + + Yields + ------ + np.ndarray + 1-D float32 array of audio samples. + + Raises + ------ + FileNotFoundError + If *wav_path* does not exist. + ValueError + If the WAV file has an unexpected sample rate. + """ + path = Path(wav_path) + if not path.exists(): + raise FileNotFoundError(f"WAV file not found: {wav_path}") + + with wave.open(str(path), "rb") as wf: + n_channels = wf.getnchannels() + sampwidth = wf.getsampwidth() + framerate = wf.getframerate() + n_frames = wf.getnframes() + + if framerate != self.sample_rate: + raise ValueError( + f"Expected sample rate {self.sample_rate}, " + f"got {framerate} in {wav_path}" + ) + + chunk_frames = int(chunk_duration * framerate) + + frames_read = 0 + while frames_read < n_frames: + to_read = min(chunk_frames, n_frames - frames_read) + raw_bytes = wf.readframes(to_read) + frames_read += to_read + + # Convert raw bytes to numpy float32 + audio = self._bytes_to_float32( + raw_bytes, n_channels=n_channels, sampwidth=sampwidth + ) + yield audio + + def generate_silence(self, duration: float) -> np.ndarray: + """Generate silent audio. + + Parameters + ---------- + duration : float + Duration in seconds. + + Returns + ------- + np.ndarray + 1-D float32 zero array. + """ + return np.zeros(int(duration * self.sample_rate), dtype=np.float32) + + def generate_tone( + self, + frequency: float = 440.0, + duration: float = 1.0, + amplitude: float = 0.5, + ) -> np.ndarray: + """Generate a simple sine-wave tone (useful for testing). + + Parameters + ---------- + frequency : float + Tone frequency in Hz. + duration : float + Duration in seconds. + amplitude : float + Peak amplitude (0.0 -- 1.0). + + Returns + ------- + np.ndarray + 1-D float32 sine wave. + """ + n_samples = int(duration * self.sample_rate) + t = np.linspace(0, duration, n_samples, dtype=np.float32) + return np.float32(amplitude) * np.sin( + np.float32(2.0 * np.pi * frequency) * t + ) + + # ------------------------------------------------------------------ + # Internals + # ------------------------------------------------------------------ + + @staticmethod + def _bytes_to_float32( + raw_bytes: bytes, + n_channels: int = 1, + sampwidth: int = 2, + ) -> np.ndarray: + """Convert raw WAV bytes to a mono float32 array in ``[-1, 1]``. + + Parameters + ---------- + raw_bytes : bytes + Raw PCM bytes from ``wave.readframes()``. + n_channels : int + Number of audio channels (1 = mono, 2 = stereo). + sampwidth : int + Sample width in bytes (1, 2, or 4). + + Returns + ------- + np.ndarray + 1-D float32 array normalised to ``[-1.0, 1.0]``. + """ + if sampwidth == 1: + dtype = np.uint8 + max_val = 128.0 + offset = 128.0 # unsigned 8-bit + elif sampwidth == 2: + dtype = np.int16 + max_val = 32768.0 + offset = 0.0 + elif sampwidth == 4: + dtype = np.int32 + max_val = 2_147_483_648.0 + offset = 0.0 + else: + raise ValueError(f"Unsupported sample width: {sampwidth}") + + samples = np.frombuffer(raw_bytes, dtype=dtype).astype(np.float32) + + if offset != 0.0: + samples = samples - offset + + samples = samples / max_val + + # Mix to mono if stereo + if n_channels > 1: + samples = samples.reshape(-1, n_channels).mean(axis=1) + + return samples diff --git a/demo/backend/live_mic.py b/demo/backend/live_mic.py new file mode 100644 index 0000000000000000000000000000000000000000..f6becf452b13811c58ba552676a9ca2582b9c6c0 --- /dev/null +++ b/demo/backend/live_mic.py @@ -0,0 +1,212 @@ +"""Live microphone audio capture for demo. + +Provides a thread-safe audio capture interface that reads from the system +microphone via PyAudio and exposes chunks through a queue. This allows +the WebSocket server to ingest live audio without blocking the event loop. + +Note: PyAudio (``pip install pyaudio``) is required for live capture. +It is an optional dependency -- the demo works fine with pre-recorded +transcripts when PyAudio is not installed. +""" + +from __future__ import annotations + +import queue +import threading +import time +from typing import Optional + +import numpy as np + + +class LiveMicCapture: + """Capture audio from the system microphone. + + Parameters + ---------- + sample_rate : int + Audio sample rate in Hz (default 16 kHz for Whisper). + chunk_size : int + Number of frames per read from the audio device. + channels : int + Number of audio channels (1 = mono). + """ + + def __init__( + self, + sample_rate: int = 16_000, + chunk_size: int = 1024, + channels: int = 1, + input_device: str | int | None = None, + ) -> None: + self.sample_rate = sample_rate + self.chunk_size = chunk_size + self.channels = channels + self.input_device = input_device + self.audio_queue: queue.Queue[np.ndarray] = queue.Queue() + self._running = False + self._thread: Optional[threading.Thread] = None + self._stream = None + self._pyaudio_instance = None + self._backend: str | None = None + + # ------------------------------------------------------------------ + # Public API + # ------------------------------------------------------------------ + + def start(self) -> None: + """Start capturing audio in a background thread. + + Uses PyAudio when available, otherwise falls back to sounddevice. + + Raises + ------ + ImportError + If neither PyAudio nor sounddevice is installed. + RuntimeError + If the capture is already running. + """ + if self._running: + raise RuntimeError("Capture is already running") + + try: + import pyaudio # noqa: F401 + self._backend = "pyaudio" + except ImportError: + try: + import sounddevice # noqa: F401 + self._backend = "sounddevice" + except ImportError as exc: + raise ImportError( + "Live microphone capture requires PyAudio or sounddevice. " + "Install one with: pip install pyaudio OR pip install sounddevice" + ) from exc + + self._running = True + self._thread = threading.Thread( + target=self._capture_loop, daemon=True, name="mic-capture" + ) + self._thread.start() + + def stop(self) -> None: + """Stop capturing and release the audio device.""" + self._running = False + if self._thread is not None: + self._thread.join(timeout=3.0) + self._thread = None + + def get_chunk(self, timeout: float = 1.0) -> Optional[np.ndarray]: + """Get next audio chunk from the queue. + + Parameters + ---------- + timeout : float + Maximum seconds to wait for a chunk. + + Returns + ------- + np.ndarray or None + Float32 audio chunk, or ``None`` if the timeout expired. + """ + try: + return self.audio_queue.get(timeout=timeout) + except queue.Empty: + return None + + def drain_queue(self) -> None: + """Drop any buffered audio chunks currently waiting in the queue.""" + while True: + try: + self.audio_queue.get_nowait() + except queue.Empty: + break + + @property + def is_running(self) -> bool: + """Whether the capture loop is active.""" + return self._running + + # ------------------------------------------------------------------ + # Internals + # ------------------------------------------------------------------ + + def _capture_loop(self) -> None: + """Background thread that reads from the microphone. + + Opens an input stream (PyAudio or sounddevice), reads chunks, + and puts float32 audio on the queue for the main application + to consume. + """ + if self._backend == "sounddevice": + self._capture_loop_sounddevice() + return + + import pyaudio + + pa = pyaudio.PyAudio() + self._pyaudio_instance = pa + + try: + stream = pa.open( + format=pyaudio.paInt16, + channels=self.channels, + rate=self.sample_rate, + input=True, + frames_per_buffer=self.chunk_size, + input_device_index=(self.input_device if isinstance(self.input_device, int) else None), + ) + self._stream = stream + + while self._running: + try: + raw_data = stream.read( + self.chunk_size, exception_on_overflow=False + ) + except OSError: + # Audio device error -- skip this chunk + continue + + # Convert int16 bytes to float32 in [-1, 1] + audio = ( + np.frombuffer(raw_data, dtype=np.int16).astype(np.float32) + / 32768.0 + ) + self.audio_queue.put(audio) + + finally: + if self._stream is not None: + try: + self._stream.stop_stream() + self._stream.close() + except OSError: + pass + self._stream = None + + pa.terminate() + self._pyaudio_instance = None + + def _capture_loop_sounddevice(self) -> None: + """Capture loop implemented with sounddevice InputStream.""" + import sounddevice as sd + + def _callback(indata, frames, _time_info, status): + if status: + return + # sounddevice already provides float32 in [-1, 1] when dtype=float32 + audio = np.asarray(indata[:, 0], dtype=np.float32).copy() + self.audio_queue.put(audio) + + try: + with sd.InputStream( + samplerate=self.sample_rate, + channels=self.channels, + dtype="float32", + blocksize=self.chunk_size, + device=self.input_device, + callback=_callback, + ) as stream: + self._stream = stream + while self._running: + time.sleep(0.05) + finally: + self._stream = None diff --git a/demo/backend/main.py b/demo/backend/main.py new file mode 100644 index 0000000000000000000000000000000000000000..2d25f59746710b2f8b54b7cb3bdfad80260819a0 --- /dev/null +++ b/demo/backend/main.py @@ -0,0 +1,1389 @@ +"""SentinelEdge Demo Backend - Real-time fraud detection server.""" + +import asyncio +import json +import re +import time +import os +import sys +import re +import urllib.request +import urllib.error +import numpy as np +from pathlib import Path +from fastapi import FastAPI, WebSocket, WebSocketDisconnect +from fastapi.middleware.cors import CORSMiddleware +import uvicorn + +# Load environment variables from .env file if present +try: + from dotenv import load_dotenv + load_dotenv() +except ImportError: + pass # python-dotenv not installed, use system env vars only + +# Add project root to path so sentinel_edge package is importable +_PROJECT_ROOT = str(Path(__file__).resolve().parent.parent.parent) +if _PROJECT_ROOT not in sys.path: + sys.path.insert(0, _PROJECT_ROOT) + +app = FastAPI(title="SentinelEdge Demo", version="0.1.0") + +INTERACTIVE_REPLY_TIMEOUT_SECONDS = 15.0 +MIN_SCAMMER_TURNS_FOR_ALERT = 3 +MIN_USER_REPLIES_FOR_ALERT = 2 +MIN_CALL_SECONDS_FOR_ALERT = 20.0 +REPLY_LISTEN_START_DELAY_SECONDS = 0.25 +VOICE_ACTIVITY_RMS_THRESHOLD = 0.0015 +MIN_VOICED_SECONDS_FOR_TRANSCRIBE = 0.3 +END_OF_UTTERANCE_SILENCE_SECONDS = 0.8 +MAX_UTTERANCE_SECONDS = 7.0 +ANTHROPIC_API_URL = "https://api.anthropic.com/v1/messages" +ANTHROPIC_API_VERSION = "2023-06-01" + + +def _read_anthropic_api_key_from_env() -> str | None: + """Read optional Anthropic key from env without hardcoding secrets.""" + key = os.getenv("SENTINEL_ANTHROPIC_API_KEY") or os.getenv("ANTHROPIC_API_KEY") + if key is None: + return None + key = key.strip() + return key if key else None + + +def _extract_anthropic_text(response_json: dict) -> str | None: + """Extract first text block from Anthropic messages response.""" + content = response_json.get("content") + if not isinstance(content, list): + return None + + for block in content: + if isinstance(block, dict) and block.get("type") == "text": + text = block.get("text") + if isinstance(text, str) and text.strip(): + return text.strip() + return None + + +def _candidate_anthropic_models(configured_model: str) -> list[str]: + """Return ordered, de-duplicated Anthropic model candidates.""" + candidates = [ + configured_model, + "claude-sonnet-4-5-latest", + "claude-sonnet-4-0", + "claude-3-7-sonnet-latest", + "claude-3-5-haiku-latest", + ] + + seen: set[str] = set() + ordered: list[str] = [] + for model in candidates: + if model and model not in seen: + seen.add(model) + ordered.append(model) + return ordered + + +def _personalize_scammer_line_sync( + *, + base_sentence: str, + call_description: str, + recent_user_replies: list[str], + recent_scammer_lines: list[str], +) -> str | None: + """Call Anthropic API and return one personalized scammer sentence.""" + api_key = _read_anthropic_api_key_from_env() + if api_key is None: + print("[Claude] API key not found in env; personalization disabled.") + return None + + # Redact key for logging (show only last 8 chars) + key_display = f"...{api_key[-8:]}" if len(api_key) > 8 else "***" + print(f"[Claude] API key loaded: {key_display}") + + configured_model = os.getenv("SENTINEL_ANTHROPIC_MODEL", "claude-sonnet-4-5-latest") + model_candidates = _candidate_anthropic_models(configured_model) + print(f"[Claude] Attempting personalization with model candidates: {model_candidates}") + user_context = " | ".join(recent_user_replies[-3:]) + scammer_context = " | ".join(recent_scammer_lines[-2:]) + + system_prompt = ( + "You are generating a scam call simulation sentence for cybersecurity training. " + "Return exactly one sentence, under 35 words, plain text only. " + "Keep it plausible for a phone scam and adapt to the victim reply context. " + "Do not include markdown, bullet points, labels, or safety disclaimers." + ) + + user_prompt = ( + f"Call scenario: {call_description}. " + f"Script baseline sentence: {base_sentence} " + f"Recent scammer lines: {scammer_context or 'none'} " + f"Recent victim replies: {user_context or 'none'}" + ) + + payload = { + "model": model_candidates[0], + "max_tokens": 80, + "temperature": 0.7, + "system": system_prompt, + "messages": [{"role": "user", "content": user_prompt}], + } + + headers = { + "x-api-key": api_key, + "anthropic-version": ANTHROPIC_API_VERSION, + "content-type": "application/json", + } + + for model in model_candidates: + payload["model"] = model + body = json.dumps(payload).encode("utf-8") + request = urllib.request.Request( + ANTHROPIC_API_URL, + data=body, + headers=headers, + method="POST", + ) + + try: + print(f"[Claude] API request to {ANTHROPIC_API_URL} using model: {model}") + with urllib.request.urlopen(request, timeout=8) as response: + response_json = json.loads(response.read().decode("utf-8")) + + text = _extract_anthropic_text(response_json) + if text is None: + print(f"[Claude] Model {model} returned empty text block.") + continue + + personalized = " ".join(text.split()) + print(f"[Claude] Personalized sentence ({model}): {personalized[:80]}...") + return personalized + except urllib.error.HTTPError as e: + error_body = e.read().decode("utf-8") if e.fp else "" + print(f"[Claude] HTTP Error {e.code} on model {model}: {e.reason}") + print(f"[Claude] Response body: {error_body[:200]}") + + is_model_not_found = ( + e.code == 404 + and "not_found_error" in error_body + and "model:" in error_body + ) + if is_model_not_found: + print(f"[Claude] Model not found: {model}. Trying next fallback.") + continue + return None + except urllib.error.URLError as e: + print(f"[Claude] URL Error on model {model}: {e.reason}") + return None + except (TimeoutError, json.JSONDecodeError) as e: + print(f"[Claude] Request error on model {model}: {e}") + return None + + print("[Claude] No usable Anthropic model found; personalization disabled for this turn.") + return None + + +async def _personalize_scammer_line( + *, + base_sentence: str, + call_description: str, + recent_user_replies: list[str], + recent_scammer_lines: list[str], +) -> str | None: + """Async wrapper for sentence personalization.""" + return await asyncio.to_thread( + _personalize_scammer_line_sync, + base_sentence=base_sentence, + call_description=call_description, + recent_user_replies=recent_user_replies, + recent_scammer_lines=recent_scammer_lines, + ) + + +def _read_input_device_from_env() -> str | int | None: + """Read optional microphone input device from SENTINEL_INPUT_DEVICE. + + Supports either an integer device index or a device name string. + """ + value = os.getenv("SENTINEL_INPUT_DEVICE") + if value is None or not value.strip(): + return None + + value = value.strip() + if value.isdigit(): + return int(value) + return value + + +def _parse_input_device_query(value: str | None) -> str | int | None: + """Parse optional input device from websocket query parameter.""" + if value is None: + return None + value = value.strip() + if not value: + return None + if value.isdigit(): + return int(value) + return value + +app.add_middleware( + CORSMiddleware, + allow_origins=["*"], + allow_methods=["*"], + allow_headers=["*"], +) + +# Track active WebSocket connections +active_connections: list[WebSocket] = [] + +# --------------------------------------------------------------------------- +# Available demo calls +# --------------------------------------------------------------------------- + +SAMPLE_CALLS = { + "live_mic": { + "file": None, + "caller": "Live microphone", + "caller_name": "Local Device Input", + "description": "Real-time microphone detection", + }, + "irs_scam": { + "file": "sample_calls/irs_scam.txt", + "caller": "+1 (800) 555-0199", + "caller_name": "Unknown Number", + "description": "IRS Impersonation Scam", + }, + "tech_support": { + "file": "sample_calls/tech_support_scam.txt", + "caller": "+1 (888) 555-0147", + "caller_name": "Microsoft Support", + "description": "Tech Support Scam", + }, + "bank_fraud": { + "file": "sample_calls/bank_fraud_scam.txt", + "caller": "+1 (800) 555-0123", + "caller_name": "Bank Security", + "description": "Bank Fraud Scam", + }, + "legitimate": { + "file": "sample_calls/legitimate_call.txt", + "caller": "+1 (555) 234-5678", + "caller_name": "Dr. Smith Office", + "description": "Legitimate Appointment Reminder", + }, + "crypto_investment": { + "file": "sample_calls/crypto_investment_scam.txt", + "caller": "+1 (833) 555-0291", + "caller_name": "Digital Asset Partners", + "description": "Crypto Investment Scam", + }, + "grandparent": { + "file": "sample_calls/grandparent_scam.txt", + "caller": "+1 (555) 867-5309", + "caller_name": "Unknown Number", + "description": "Grandparent Scam", + }, + "amazon_refund": { + "file": "sample_calls/amazon_refund_scam.txt", + "caller": "+1 (888) 555-0342", + "caller_name": "Amazon Support", + "description": "Amazon Refund Scam", + }, + "utility_shutoff": { + "file": "sample_calls/utility_shutoff_scam.txt", + "caller": "+1 (800) 555-0476", + "caller_name": "Utility Company", + "description": "Utility Shutoff Scam", + }, + "prize_notification": { + "file": "sample_calls/prize_notification_scam.txt", + "caller": "+1 (877) 555-0188", + "caller_name": "National Sweepstakes", + "description": "Prize Notification Scam", + }, +} + +# --------------------------------------------------------------------------- +# REST endpoints +# --------------------------------------------------------------------------- + + +@app.get("/api/calls") +async def list_calls(): + """List available demo calls.""" + return {"calls": [{"id": k, **v} for k, v in SAMPLE_CALLS.items()]} + + +@app.get("/api/health") +async def health_check(): + """Health check endpoint.""" + return {"status": "ok", "active_connections": len(active_connections)} + + +@app.get("/api/audio-devices") +async def list_audio_devices(): + """List available input audio devices (when sounddevice is installed).""" + try: + import sounddevice as sd + + devices = [] + for idx, dev in enumerate(sd.query_devices()): + if dev.get("max_input_channels", 0) > 0: + devices.append( + { + "index": idx, + "name": dev.get("name", "unknown"), + "max_input_channels": int(dev.get("max_input_channels", 0)), + "default_samplerate": float(dev.get("default_samplerate", 0.0)), + } + ) + return {"devices": devices} + except Exception as exc: + return {"devices": [], "error": str(exc)} + + +# --------------------------------------------------------------------------- +# WebSocket: real-time call fraud detection +# --------------------------------------------------------------------------- + + +@app.websocket("/ws/call/{call_id}") +async def call_detection(websocket: WebSocket, call_id: str): + """WebSocket endpoint for real-time call fraud detection.""" + await websocket.accept() + active_connections.append(websocket) + + try: + if call_id not in SAMPLE_CALLS: + await websocket.send_json({"error": f"Unknown call: {call_id}"}) + return + + call_info = SAMPLE_CALLS[call_id] + + await websocket.send_json( + { + "type": "call_start", + "caller": call_info["caller"], + "caller_name": call_info["caller_name"], + "description": call_info["description"], + "timestamp": time.time(), + } + ) + + input_device = _parse_input_device_query( + websocket.query_params.get("input_device") + ) + + if call_id == "live_mic": + await run_live_mic_detection(websocket, input_device=input_device) + return + + # Load transcript sentences + transcript_path = os.path.join(os.path.dirname(__file__), call_info["file"]) + sentences = load_transcript(transcript_path) + + interactive = websocket.query_params.get("interactive") == "1" + if interactive: + await run_interactive_scripted_call( + websocket, + sentences, + input_device=input_device, + ) + return + + # Import detection pipeline components + from sentinel_edge.features.handcrafted import extract_handcrafted_features + from sentinel_edge.features.feature_pipeline import FeaturePipeline + from sentinel_edge.classifier.xgb_classifier import FraudClassifier + from sentinel_edge.classifier.score_accumulator import ScoreAccumulator + from sentinel_edge.classifier.alert_engine import AlertEngine + + accumulator = ScoreAccumulator(alpha=0.3) + alert_engine = AlertEngine() + + _model_path = os.path.join(_PROJECT_ROOT, "models", "call_fraud_xgb.json") + _tfidf_path = os.path.join(_PROJECT_ROOT, "models", "tfidf_call_vectorizer.pkl") + _use_real_model = os.path.exists(_model_path) and os.path.exists(_tfidf_path) + if _use_real_model: + _classifier = FraudClassifier(_model_path) + _pipeline = FeaturePipeline(_tfidf_path) + else: + _classifier = None + _pipeline = None + + call_start_time = time.time() + + for i, sentence in enumerate(sentences): + await asyncio.sleep(1.5 + np.random.random() * 1.5) + + features = extract_handcrafted_features(sentence) + + if _use_real_model: + t0 = time.perf_counter() + feature_vec = _pipeline.extract(sentence) + fraud_score = _classifier.predict_proba(feature_vec) + _inference_ms = (time.perf_counter() - t0) * 1000 + else: + fraud_score = compute_heuristic_score(features) + _inference_ms = np.random.uniform(5, 15) + + ema_score = accumulator.update(fraud_score) + alert = alert_engine.evaluate(ema_score, features) + elapsed = time.time() - call_start_time + + await websocket.send_json( + { + "type": "sentence", + "speaker": "scammer", + "index": i, + "text": sentence, + "raw_score": round(fraud_score, 4), + "ema_score": round(ema_score, 4), + "features": { + k: round(v, 4) if isinstance(v, float) else int(v) + for k, v in features.items() + }, + "alert": { + "should_alert": alert.should_alert, + "risk_level": alert.risk_level.value, + "reasons": alert.reasons, + }, + "elapsed_seconds": round(elapsed, 1), + "inference_ms": round(_inference_ms, 1), + "timestamp": time.time(), + } + ) + + try: + msg = await asyncio.wait_for( + websocket.receive_text(), timeout=0.01 + ) + data = json.loads(msg) + if data.get("action") == "block": + await websocket.send_json( + {"type": "call_blocked", "timestamp": time.time()} + ) + return + elif data.get("action") == "dismiss": + pass + except asyncio.TimeoutError: + pass + except json.JSONDecodeError: + pass + + await websocket.send_json( + { + "type": "call_end", + "final_score": round(accumulator.current_score, 4), + "peak_score": round(accumulator.peak_score, 4), + "mean_score": round(accumulator.mean_score, 4), + "total_sentences": len(sentences), + "duration_seconds": round(time.time() - call_start_time, 1), + "timestamp": time.time(), + } + ) + + except WebSocketDisconnect: + pass + except Exception as exc: + try: + await websocket.send_json( + {"type": "error", "message": str(exc), "timestamp": time.time()} + ) + except Exception: + pass + finally: + if websocket in active_connections: + active_connections.remove(websocket) + + +async def run_interactive_scripted_call( + websocket: WebSocket, + scripted_sentences: list[str], + input_device: str | int | None = None, +) -> None: + """Turn-based scripted call: scammer line, wait for user reply, repeat.""" + from live_mic import LiveMicCapture + from sentinel_edge.audio.transcriber import Transcriber + from sentinel_edge.engine import SentinelEngine + + engine = SentinelEngine(models_dir=os.path.join(_PROJECT_ROOT, "models")) + engine.reset_call_state() + mic = LiveMicCapture( + sample_rate=16_000, + chunk_size=2048, + channels=1, + input_device=(input_device if input_device is not None else _read_input_device_from_env()), + ) + whisper_model = os.getenv("SENTINEL_WHISPER_MODEL", "base.en") + transcriber = Transcriber(model_name=whisper_model) + + call_start_time = time.time() + turn_index = 0 + scammer_turn_count = 0 + user_reply_count = 0 + fraud_alert_sent = False + user_replies: list[str] = [] + scammer_history: list[str] = [] + last_scammer_sentence = "" + + try: + mic.start() + + for scam_sentence in scripted_sentences: + await asyncio.sleep(1.0) + + line_to_send = scam_sentence + if user_replies: + print(f"[Main] Attempting personalization (user has {len(user_replies)} replies)") + personalized = await _personalize_scammer_line( + base_sentence=scam_sentence, + call_description="Interactive scam call simulation", + recent_user_replies=user_replies, + recent_scammer_lines=scammer_history, + ) + if personalized: + line_to_send = personalized + else: + print(f"[Main] No user replies yet; skipping personalization") + + t0 = time.perf_counter() + fraud_score, features = engine.analyze_sentence(line_to_send) + ema_score = engine.accumulator.update(fraud_score) + alert = engine.alert_engine.evaluate(ema_score, features) + inference_ms = (time.perf_counter() - t0) * 1000.0 + + await websocket.send_json( + { + "type": "sentence", + "speaker": "scammer", + "index": turn_index, + "text": line_to_send, + "raw_score": round(fraud_score, 4), + "ema_score": round(ema_score, 4), + "features": { + k: round(v, 4) if isinstance(v, float) else int(v) + for k, v in features.items() + }, + "alert": { + "should_alert": alert.should_alert, + "risk_level": alert.risk_level.value, + "reasons": alert.reasons, + }, + "elapsed_seconds": round(time.time() - call_start_time, 1), + "inference_ms": round(inference_ms, 2), + "timestamp": time.time(), + } + ) + turn_index += 1 + scammer_turn_count += 1 + last_scammer_sentence = line_to_send + scammer_history.append(line_to_send) + + elapsed_seconds = time.time() - call_start_time + if ( + not fraud_alert_sent + and alert.should_alert + and scammer_turn_count >= MIN_SCAMMER_TURNS_FOR_ALERT + and user_reply_count >= MIN_USER_REPLIES_FOR_ALERT + and elapsed_seconds >= MIN_CALL_SECONDS_FOR_ALERT + ): + fraud_alert_sent = True + await websocket.send_json( + { + "type": "fraud_detected", + "message": "High scam risk detected. Hang up now.", + "risk_level": alert.risk_level.value, + "ema_score": round(ema_score, 4), + "reasons": alert.reasons, + "timestamp": time.time(), + } + ) + + await websocket.send_json( + { + "type": "waiting_for_reply", + "timeout_seconds": int(INTERACTIVE_REPLY_TIMEOUT_SECONDS), + "timestamp": time.time(), + } + ) + + # Ensure only fresh user turn audio is considered. + mic.drain_queue() + + user_sentence, heard_audio = await _capture_user_sentence( + websocket=websocket, + mic=mic, + transcriber=transcriber, + timeout_seconds=INTERACTIVE_REPLY_TIMEOUT_SECONDS, + listen_start_delay_seconds=REPLY_LISTEN_START_DELAY_SECONDS, + voice_activity_rms_threshold=VOICE_ACTIVITY_RMS_THRESHOLD, + min_voiced_seconds_for_transcribe=MIN_VOICED_SECONDS_FOR_TRANSCRIBE, + ) + + if not heard_audio: + await websocket.send_json( + { + "type": "error", + "message": ( + "No microphone audio detected during reply window. " + "Check OS microphone permission and input device selection." + ), + "timestamp": time.time(), + } + ) + continue + + if user_sentence is None: + await websocket.send_json( + { + "type": "user_timeout", + "message": "No reply detected, continuing call.", + "timestamp": time.time(), + } + ) + continue + + if _looks_like_echo(user_sentence, last_scammer_sentence): + await websocket.send_json( + { + "type": "user_echo_detected", + "message": "Detected speaker echo. Use headphones or lower volume and repeat.", + "timestamp": time.time(), + } + ) + continue + + # Score user reply for display only. Do not mix into scammer EMA. + user_score, user_features = engine.analyze_sentence(user_sentence) + user_replies.append(user_sentence) + user_reply_count += 1 + await websocket.send_json( + { + "type": "sentence", + "speaker": "you", + "index": turn_index, + "text": user_sentence, + "raw_score": round(user_score, 4), + "ema_score": round(engine.accumulator.current_score, 4), + "features": { + k: round(v, 4) if isinstance(v, float) else int(v) + for k, v in user_features.items() + }, + "alert": { + "should_alert": False, + "risk_level": "safe", + "reasons": [], + }, + "elapsed_seconds": round(time.time() - call_start_time, 1), + "inference_ms": 0.0, + "timestamp": time.time(), + } + ) + turn_index += 1 + + # Allow client action such as manual block/hangup. + try: + msg = await asyncio.wait_for(websocket.receive_text(), timeout=0.01) + data = json.loads(msg) + if data.get("action") == "block": + await websocket.send_json( + {"type": "call_blocked", "timestamp": time.time()} + ) + break + except asyncio.TimeoutError: + pass + except json.JSONDecodeError: + pass + + await websocket.send_json( + { + "type": "call_end", + "final_score": round(engine.accumulator.current_score, 4), + "peak_score": round(engine.accumulator.peak_score, 4), + "mean_score": round(engine.accumulator.mean_score, 4), + "total_sentences": turn_index, + "duration_seconds": round(time.time() - call_start_time, 1), + "timestamp": time.time(), + } + ) + finally: + mic.stop() + + +async def _capture_user_sentence( + websocket: WebSocket, + mic, + transcriber, + timeout_seconds: float, + listen_start_delay_seconds: float, + voice_activity_rms_threshold: float, + min_voiced_seconds_for_transcribe: float, +) -> tuple[str | None, bool]: + """Capture microphone input and return one completed user sentence.""" + from sentinel_edge.audio.sentence_splitter import SentenceSplitter + + splitter = SentenceSplitter() + utterance_chunks: list[np.ndarray] = [] + utterance_samples = 0 + min_samples = int(16_000 * min_voiced_seconds_for_transcribe) + max_samples = int(16_000 * MAX_UTTERANCE_SECONDS) + deadline = time.time() + timeout_seconds + listen_start = time.time() + listen_start_delay_seconds + heard_audio = False + speech_started = False + silence_run_seconds = 0.0 + + while time.time() < deadline: + # Handle immediate client actions while waiting for reply. + try: + msg = await asyncio.wait_for(websocket.receive_text(), timeout=0.01) + data = json.loads(msg) + if data.get("action") == "block": + return None, heard_audio + except asyncio.TimeoutError: + pass + except json.JSONDecodeError: + pass + + chunk = mic.get_chunk(timeout=0.2) + if chunk is None: + await asyncio.sleep(0.01) + continue + + if time.time() < listen_start: + # Avoid immediately picking up system TTS bleed-through. + continue + + heard_audio = True + + rms = float(np.sqrt(np.mean(np.square(chunk)))) + chunk_seconds = len(chunk) / 16_000.0 + + if rms < voice_activity_rms_threshold: + if speech_started: + # Keep trailing silence so Whisper captures final words naturally. + utterance_chunks.append(chunk) + utterance_samples += len(chunk) + silence_run_seconds += chunk_seconds + if ( + utterance_samples >= min_samples + and silence_run_seconds >= END_OF_UTTERANCE_SILENCE_SECONDS + ): + break + continue + + speech_started = True + silence_run_seconds = 0.0 + utterance_chunks.append(chunk) + utterance_samples += len(chunk) + if utterance_samples >= max_samples: + break + + if utterance_chunks and utterance_samples >= min_samples: + audio = np.concatenate(utterance_chunks) + transcript = transcriber.transcribe(audio, sample_rate=16_000).strip() + if transcript: + sentences = splitter.feed(transcript) + if sentences: + return sentences[0], heard_audio + + # Whisper can return partial text without sentence punctuation. + if len(transcript.split()) >= 1: + return transcript, heard_audio + + # Fallback: if we heard audio but speech never crossed VAD, try a short best-effort + # transcription on what we did capture to handle very quiet microphones. + if utterance_chunks and heard_audio: + audio = np.concatenate(utterance_chunks) + transcript = transcriber.transcribe(audio, sample_rate=16_000).strip() + if transcript: + return transcript, heard_audio + + leftover = splitter.flush() + if leftover: + return leftover, heard_audio + return None, heard_audio + + +def _looks_like_echo(user_sentence: str, scam_sentence: str) -> bool: + """Heuristic guard: detect if user transcript likely matches scammer TTS echo.""" + def _normalize(text: str) -> list[str]: + cleaned = re.sub(r"[^a-z0-9\s]", " ", text.lower()) + tokens = [t for t in cleaned.split() if len(t) > 2] + return tokens + + user_tokens = _normalize(user_sentence) + scam_tokens = _normalize(scam_sentence) + + if not user_tokens or not scam_tokens: + return False + + user_set = set(user_tokens) + scam_set = set(scam_tokens) + overlap = len(user_set & scam_set) + ratio = overlap / max(len(user_set), 1) + + # Require substantial overlap to reduce false positives on short user replies. + return overlap >= 4 and ratio >= 0.85 + + +async def run_live_mic_detection( + websocket: WebSocket, + input_device: str | int | None = None, +) -> None: + """Run live microphone -> transcription -> model scoring pipeline.""" + from live_mic import LiveMicCapture + from sentinel_edge.audio.sentence_splitter import SentenceSplitter + from sentinel_edge.audio.transcriber import Transcriber + from sentinel_edge.engine import SentinelEngine + + mic = LiveMicCapture( + sample_rate=16_000, + chunk_size=2048, + channels=1, + input_device=(input_device if input_device is not None else _read_input_device_from_env()), + ) + splitter = SentenceSplitter() + whisper_model = os.getenv("SENTINEL_WHISPER_MODEL", "base.en") + transcriber = Transcriber(model_name=whisper_model) + engine = SentinelEngine(models_dir=os.path.join(_PROJECT_ROOT, "models")) + engine.reset_call_state() + + call_start_time = time.time() + sentence_index = 0 + chunk_accumulator: list[np.ndarray] = [] + accumulated_samples = 0 + # Transcribe roughly every 2 seconds for stable Whisper context. + target_samples = 16_000 * 2 + + try: + mic.start() + + while True: + # Non-blocking action handling from client. + try: + msg = await asyncio.wait_for(websocket.receive_text(), timeout=0.01) + data = json.loads(msg) + if data.get("action") == "block": + await websocket.send_json( + {"type": "call_blocked", "timestamp": time.time()} + ) + break + except asyncio.TimeoutError: + pass + except json.JSONDecodeError: + pass + + chunk = mic.get_chunk(timeout=0.2) + if chunk is None: + await asyncio.sleep(0.01) + continue + + chunk_accumulator.append(chunk) + accumulated_samples += len(chunk) + + if accumulated_samples < target_samples: + continue + + audio = np.concatenate(chunk_accumulator) + chunk_accumulator = [] + accumulated_samples = 0 + + transcript = transcriber.transcribe(audio, sample_rate=16_000).strip() + if not transcript: + continue + + for sentence in splitter.feed(transcript): + t0 = time.perf_counter() + fraud_score, features = engine.analyze_sentence(sentence) + ema_score = engine.accumulator.update(fraud_score) + alert = engine.alert_engine.evaluate(ema_score, features) + inference_ms = (time.perf_counter() - t0) * 1000.0 + + await websocket.send_json( + { + "type": "sentence", + "index": sentence_index, + "text": sentence, + "raw_score": round(fraud_score, 4), + "ema_score": round(ema_score, 4), + "features": { + k: round(v, 4) if isinstance(v, float) else int(v) + for k, v in features.items() + }, + "alert": { + "should_alert": alert.should_alert, + "risk_level": alert.risk_level.value, + "reasons": alert.reasons, + }, + "elapsed_seconds": round(time.time() - call_start_time, 1), + "inference_ms": round(inference_ms, 2), + "timestamp": time.time(), + } + ) + sentence_index += 1 + + leftover = splitter.flush() + if leftover: + t0 = time.perf_counter() + fraud_score, features = engine.analyze_sentence(leftover) + ema_score = engine.accumulator.update(fraud_score) + alert = engine.alert_engine.evaluate(ema_score, features) + inference_ms = (time.perf_counter() - t0) * 1000.0 + await websocket.send_json( + { + "type": "sentence", + "index": sentence_index, + "text": leftover, + "raw_score": round(fraud_score, 4), + "ema_score": round(ema_score, 4), + "features": { + k: round(v, 4) if isinstance(v, float) else int(v) + for k, v in features.items() + }, + "alert": { + "should_alert": alert.should_alert, + "risk_level": alert.risk_level.value, + "reasons": alert.reasons, + }, + "elapsed_seconds": round(time.time() - call_start_time, 1), + "inference_ms": round(inference_ms, 2), + "timestamp": time.time(), + } + ) + sentence_index += 1 + + await websocket.send_json( + { + "type": "call_end", + "final_score": round(engine.accumulator.current_score, 4), + "peak_score": round(engine.accumulator.peak_score, 4), + "mean_score": round(engine.accumulator.mean_score, 4), + "total_sentences": sentence_index, + "duration_seconds": round(time.time() - call_start_time, 1), + "timestamp": time.time(), + } + ) + finally: + mic.stop() + + +# --------------------------------------------------------------------------- +# WebSocket: live microphone fraud detection +# --------------------------------------------------------------------------- + +_LIVE_MIC_BUFFER_DURATION = 5.0 + + +@app.websocket("/ws/call/live") +async def live_mic_detection(websocket: WebSocket): + """WebSocket endpoint for live microphone fraud detection.""" + await websocket.accept() + active_connections.append(websocket) + + mic = None + try: + try: + from demo.backend.live_mic import LiveMicCapture + except ImportError as e: + await websocket.send_json({ + "type": "error", + "message": f"Missing dependency for live mic: {e}", + "timestamp": time.time(), + }) + return + + try: + from sentinel_edge.audio.transcriber import Transcriber + except ImportError as e: + await websocket.send_json({ + "type": "error", + "message": f"Missing dependency for transcription: {e}", + "timestamp": time.time(), + }) + return + + mic = LiveMicCapture(sample_rate=16000) + transcriber = Transcriber(model_name="tiny.en") + + if not transcriber.is_loaded: + await websocket.send_json({ + "type": "status", + "message": "Loading Whisper model (may download on first use)...", + "timestamp": time.time(), + }) + + try: + transcriber.load() + except Exception as e: + await websocket.send_json({ + "type": "error", + "message": ( + f"Failed to load Whisper model: {e}. " + "Install with: pip install openai-whisper" + ), + "timestamp": time.time(), + }) + return + + from sentinel_edge.features.handcrafted import extract_handcrafted_features + from sentinel_edge.features.feature_pipeline import FeaturePipeline + from sentinel_edge.classifier.xgb_classifier import FraudClassifier as XGBFraudClassifier + from sentinel_edge.classifier.score_accumulator import ScoreAccumulator + from sentinel_edge.classifier.alert_engine import AlertEngine + + accumulator = ScoreAccumulator(alpha=0.3) + alert_engine = AlertEngine() + + _model_path = os.path.join(_PROJECT_ROOT, "models", "call_fraud_xgb.json") + _tfidf_path = os.path.join(_PROJECT_ROOT, "models", "tfidf_call_vectorizer.pkl") + _use_real_model = os.path.exists(_model_path) and os.path.exists(_tfidf_path) + if _use_real_model: + _classifier = XGBFraudClassifier(_model_path) + _pipeline = FeaturePipeline(_tfidf_path) + else: + _classifier = None + _pipeline = None + + try: + mic.start() + except ImportError as e: + await websocket.send_json({ + "type": "error", + "message": str(e), + "timestamp": time.time(), + }) + return + except Exception as e: + await websocket.send_json({ + "type": "error", + "message": f"Failed to start microphone: {e}", + "timestamp": time.time(), + }) + return + + await websocket.send_json({ + "type": "call_start", + "caller": "Live Microphone", + "caller_name": "Live Input", + "description": "Live Microphone Analysis", + "timestamp": time.time(), + }) + + call_start_time = time.time() + sentence_index = 0 + audio_buffer: list[np.ndarray] = [] + buffered_seconds = 0.0 + + while True: + try: + msg = await asyncio.wait_for( + websocket.receive_text(), timeout=0.05 + ) + data = json.loads(msg) + if data.get("action") in ("stop", "block"): + if data.get("action") == "block": + await websocket.send_json({ + "type": "call_blocked", + "timestamp": time.time(), + }) + break + except asyncio.TimeoutError: + pass + except json.JSONDecodeError: + pass + + chunk = await asyncio.get_event_loop().run_in_executor( + None, mic.get_chunk, 0.1 + ) + if chunk is not None: + audio_buffer.append(chunk) + buffered_seconds += len(chunk) / mic.sample_rate + + if buffered_seconds >= _LIVE_MIC_BUFFER_DURATION and audio_buffer: + audio_segment = np.concatenate(audio_buffer) + audio_buffer.clear() + buffered_seconds = 0.0 + + transcript = await asyncio.get_event_loop().run_in_executor( + None, transcriber.transcribe, audio_segment, 16000 + ) + + if not transcript.strip(): + continue + + sentences = _split_sentences(transcript) + + for sentence in sentences: + sentence = sentence.strip() + if not sentence: + continue + + features = extract_handcrafted_features(sentence) + + if _use_real_model: + t0 = time.perf_counter() + feature_vec = _pipeline.extract(sentence) + fraud_score = _classifier.predict_proba(feature_vec) + _inference_ms = (time.perf_counter() - t0) * 1000 + else: + fraud_score = compute_heuristic_score(features) + _inference_ms = np.random.uniform(5, 15) + + ema_score = accumulator.update(fraud_score) + alert = alert_engine.evaluate(ema_score, features) + elapsed = time.time() - call_start_time + + await websocket.send_json({ + "type": "sentence", + "index": sentence_index, + "text": sentence, + "raw_score": round(fraud_score, 4), + "ema_score": round(ema_score, 4), + "features": { + k: round(v, 4) if isinstance(v, float) else int(v) + for k, v in features.items() + }, + "alert": { + "should_alert": alert.should_alert, + "risk_level": alert.risk_level.value, + "reasons": alert.reasons, + }, + "elapsed_seconds": round(elapsed, 1), + "inference_ms": round(_inference_ms, 1), + "timestamp": time.time(), + }) + sentence_index += 1 + + await websocket.send_json({ + "type": "call_end", + "final_score": round(accumulator.current_score, 4), + "peak_score": round(accumulator.peak_score, 4), + "mean_score": round(accumulator.mean_score, 4), + "total_sentences": sentence_index, + "duration_seconds": round(time.time() - call_start_time, 1), + "timestamp": time.time(), + }) + + except WebSocketDisconnect: + pass + except Exception as exc: + try: + await websocket.send_json({ + "type": "error", + "message": str(exc), + "timestamp": time.time(), + }) + except Exception: + pass + finally: + if mic is not None and mic.is_running: + mic.stop() + if websocket in active_connections: + active_connections.remove(websocket) + + +def _split_sentences(text: str) -> list[str]: + """Split transcribed text into individual sentences.""" + parts = re.split(r'(?<=[.!?])\s+', text.strip()) + return [p for p in parts if p.strip()] + + +# --------------------------------------------------------------------------- +# WebSocket: privacy / federated learning demo +# --------------------------------------------------------------------------- + + +@app.websocket("/ws/privacy-demo") +async def privacy_demo(websocket: WebSocket): + """Show what the hub sees vs what stays on device.""" + await websocket.accept() + + try: + examples = [ + "This is the IRS calling about your unpaid tax debt of five thousand dollars.", + "Your social security number has been suspended due to suspicious activity.", + "Press 1 now to speak with an agent or a warrant will be issued for your arrest.", + "You need to purchase gift cards and read us the numbers to resolve this matter.", + ] + + from sentinel_edge.features.handcrafted import extract_handcrafted_features + from sentinel_edge.privacy.dp_noise import DPNoiseInjector + + n_local_samples = 50 + dp = DPNoiseInjector(epsilon=0.3) + + for idx, sentence in enumerate(examples): + await asyncio.sleep(2.0) + + features = extract_handcrafted_features(sentence) + feature_vector = np.array(list(features.values()), dtype=np.float64) + + gradient = np.random.randn(len(feature_vector)) * 0.01 + clipped = dp.clip_gradient(gradient) + noised_gradient = dp.add_noise(clipped, n_local_samples=n_local_samples) + sensitivity = 1.0 / n_local_samples + sigma = dp.compute_sigma(sensitivity) + + await websocket.send_json( + { + "type": "privacy_comparison", + "index": idx, + "on_device": { + "transcript": sentence, + "features": { + k: round(v, 4) if isinstance(v, float) else int(v) + for k, v in features.items() + }, + "fraud_score": round( + float(np.random.uniform(0.6, 0.95)), 4 + ), + }, + "hub_sees": { + "gradient_vector": [ + round(float(x), 6) for x in noised_gradient[:20] + ], + "gradient_size": len(noised_gradient), + "dp_sigma": round(float(sigma), 6), + "dp_epsilon": 0.3, + "n_samples": n_local_samples, + "model_version": 3, + }, + "timestamp": time.time(), + } + ) + + await websocket.send_json({"type": "demo_complete"}) + + except WebSocketDisconnect: + pass + except Exception as exc: + try: + await websocket.send_json( + {"type": "error", "message": str(exc), "timestamp": time.time()} + ) + except Exception: + pass + + +# --------------------------------------------------------------------------- +# Transcript loading helpers +# --------------------------------------------------------------------------- + + +def load_transcript(file_path: str) -> list[str]: + """Load transcript sentences from a text file (one sentence per line).""" + if not os.path.exists(file_path): + return get_fallback_transcript(file_path) + + with open(file_path) as f: + lines = [line.strip() for line in f if line.strip()] + return lines + + +def get_fallback_transcript(file_path: str) -> list[str]: + """Return a built-in transcript when the text file hasn't been created yet.""" + name = os.path.basename(file_path) + + if "irs" in name: + return [ + "Hello, this is Agent Williams from the Internal Revenue Service.", + "We are calling because there is a problem with your tax return.", + "Our records indicate that you owe the IRS three thousand five hundred dollars in back taxes.", + "If this amount is not paid immediately, a warrant will be issued for your arrest.", + "This is your final notice before legal action is taken against you.", + "You can resolve this matter today by making a payment over the phone.", + "We accept payment through gift cards, wire transfer, or cryptocurrency.", + "Please provide your social security number so we can verify your identity.", + "Time is running out. You must act now to avoid criminal prosecution.", + "Press 1 to be connected to a payment specialist immediately.", + ] + elif "tech" in name: + return [ + "Hello, this is the Microsoft Windows technical support department.", + "We have detected that your computer has been compromised by malicious software.", + "Hackers are currently accessing your personal files and banking information.", + "You need to act immediately to prevent further damage to your system.", + "I will need you to download our remote access tool so we can fix this problem.", + "Please go to this website and enter the access code I give you.", + "We need to verify your identity. Can you provide your email password?", + "There is a one-time security fee of two hundred and ninety nine dollars.", + "You can pay with a credit card or by purchasing Google Play gift cards.", + "If you do not resolve this now, your computer will be permanently locked.", + ] + elif "bank" in name: + return [ + "Good afternoon, this is the fraud prevention department at your bank.", + "We have detected suspicious activity on your account ending in four seven eight two.", + "Someone attempted to make a large purchase of fifteen hundred dollars.", + "For your security, we need to verify your identity immediately.", + "Can you please confirm your full account number and routing number?", + "We also need the three-digit security code on the back of your card.", + "I'm going to send you a verification code. Please read it back to me.", + "We need to transfer your funds to a secure account to protect them.", + "Please do not hang up or contact your branch directly as this is time-sensitive.", + "Your account will be frozen if we cannot complete this verification process.", + ] + else: + return [ + "Hi, this is Sarah from Doctor Smith's office calling.", + "I'm calling to confirm your appointment scheduled for next Tuesday at two thirty.", + "Doctor Smith will be performing your annual checkup.", + "Please remember to bring your insurance card and photo ID.", + "Also, please arrive fifteen minutes early to fill out any updated paperwork.", + "If you need to reschedule, please call us back at your convenience.", + "We look forward to seeing you. Have a great day!", + ] + + +# --------------------------------------------------------------------------- +# Heuristic fraud scorer (substitutes for XGBoost in demo mode) +# --------------------------------------------------------------------------- + + +def compute_heuristic_score(features: dict[str, float]) -> float: + """Compute a heuristic fraud score from handcrafted features.""" + score = 0.0 + weights = { + "urgency_count": 0.08, + "action_count": 0.06, + "financial_count": 0.07, + "impersonation_count": 0.10, + "has_url": 0.04, + "has_shortened_url": 0.06, + "has_verify_pattern": 0.04, + "has_threat": 0.10, + "has_prize": 0.06, + "has_account_ref": 0.07, + "dollar_sign": 0.03, + "has_phone_number": 0.02, + "exclamation_count": 0.02, + "caps_ratio": 0.03, + } + + for feature, weight in weights.items(): + value = features.get(feature, 0) + if isinstance(value, bool): + value = 1.0 if value else 0.0 + score += value * weight + + score = max(0.0, min(1.0, score)) + score += np.random.normal(0, 0.02) + score = max(0.0, min(1.0, score)) + + return score + + +# --------------------------------------------------------------------------- +# Entry point +# --------------------------------------------------------------------------- + +if __name__ == "__main__": + uvicorn.run( + "main:app", + host="0.0.0.0", + port=8000, + reload=True, + reload_dirs=[os.path.dirname(__file__)], + ) diff --git a/demo/backend/sample_calls/amazon_refund_scam.txt b/demo/backend/sample_calls/amazon_refund_scam.txt new file mode 100644 index 0000000000000000000000000000000000000000..01c83feea7d1b5d2a7bd108033dac734157cc2e1 --- /dev/null +++ b/demo/backend/sample_calls/amazon_refund_scam.txt @@ -0,0 +1,12 @@ +Hello, this is the Amazon customer service refund department calling. +We are calling to inform you that a charge of four hundred and ninety nine dollars was made on your Amazon account today. +This appears to be an unauthorized purchase of an Apple MacBook Pro. +If you did not make this purchase, we need to process a refund immediately. +To verify your identity and process the refund, I will need to connect to your computer remotely. +Please go to the website I'm about to give you and download our secure support tool. +Once connected, we can access your Amazon account and reverse the charge right away. +I will also need you to log into your online banking so we can verify the refund was processed correctly. +It looks like the refund went through but we accidentally sent you forty nine hundred dollars instead of four ninety nine. +You will need to send back the difference using a wire transfer to our corporate account. +This needs to be resolved today or your Amazon account will be permanently suspended. +Can you please stay on the line while we complete this process? diff --git a/demo/backend/sample_calls/bank_fraud_scam.txt b/demo/backend/sample_calls/bank_fraud_scam.txt new file mode 100644 index 0000000000000000000000000000000000000000..d36974bd69e2bc52e82d1ead06ddc5516f1590d4 --- /dev/null +++ b/demo/backend/sample_calls/bank_fraud_scam.txt @@ -0,0 +1,11 @@ +Good afternoon, this is the fraud prevention department at your bank. +We have detected suspicious activity on your account ending in four seven eight two. +Someone attempted to make an unauthorized purchase of fifteen hundred dollars. +For your security, we need to verify your identity immediately. +Can you please confirm your full account number and routing number. +We also need the three digit security code on the back of your debit card. +I am going to send you a verification code and please read it back to me. +We need to transfer your funds to a secure temporary account to protect them from the fraudster. +Please do not hang up or contact your branch directly as this is time sensitive. +Your account will be permanently frozen if we cannot complete this verification process now. +Remember, do not share this call information with anyone for security purposes. diff --git a/demo/backend/sample_calls/crypto_investment_scam.txt b/demo/backend/sample_calls/crypto_investment_scam.txt new file mode 100644 index 0000000000000000000000000000000000000000..0c4f785db39c2d7856ff7aba1b95ed7acfa116e9 --- /dev/null +++ b/demo/backend/sample_calls/crypto_investment_scam.txt @@ -0,0 +1,12 @@ +Hello, my name is David Chen and I'm a senior investment advisor at Digital Asset Partners. +I'm reaching out to a select group of individuals about an exclusive cryptocurrency opportunity. +Our proprietary AI trading algorithm has been generating returns of over five hundred percent in just three months. +We have limited spots available and this opportunity closes at midnight tonight. +The minimum investment is just two thousand dollars to get started with our managed portfolio. +Your funds will be placed in a secure digital wallet that our team manages on your behalf. +I just need your bank account details to initiate the transfer and secure your spot. +We also accept payment through Bitcoin, wire transfer, or Zelle for faster processing. +Several of our clients have already turned five thousand dollars into over fifty thousand. +I can send you a link right now to complete your registration and fund your account. +Don't miss out on this opportunity, the market conditions are perfect right now. +Can you confirm your full name and date of birth so I can set up your investor profile? diff --git a/demo/backend/sample_calls/grandparent_scam.txt b/demo/backend/sample_calls/grandparent_scam.txt new file mode 100644 index 0000000000000000000000000000000000000000..1d0c1b3c58c76c7f3eb05b08e4801dfe6d04ccd1 --- /dev/null +++ b/demo/backend/sample_calls/grandparent_scam.txt @@ -0,0 +1,12 @@ +Grandma, it's me, please don't hang up. +I'm in really big trouble and I didn't know who else to call. +I was in a car accident and the police arrested me because they said it was my fault. +Please don't tell mom and dad, they would be so upset with me. +My lawyer says I need to post bail of three thousand dollars to get out of jail today. +I'm so scared, grandma, I've never been in trouble like this before. +The lawyer said you can send the money through a wire transfer or gift cards. +His name is attorney Robert Miller and he's going to call you in a few minutes with the details. +Please don't tell anyone about this, I'm so embarrassed. +I need the money as soon as possible or they're going to keep me here overnight. +Can you go to the store and get Google Play gift cards and read me the numbers on the back? +I promise I'll pay you back as soon as I get out, I love you grandma. diff --git a/demo/backend/sample_calls/irs_scam.txt b/demo/backend/sample_calls/irs_scam.txt new file mode 100644 index 0000000000000000000000000000000000000000..6535a0d7fe80aeb1e1f4da1ab0c327b1fdf73fd3 --- /dev/null +++ b/demo/backend/sample_calls/irs_scam.txt @@ -0,0 +1,11 @@ +Hello, this is Agent Williams from the Internal Revenue Service. +We are calling because there is a serious problem with your federal tax return. +Our records indicate that you owe the IRS three thousand five hundred dollars in unpaid back taxes. +If this amount is not paid immediately, a warrant will be issued for your arrest today. +This is your final notice before legal action is taken against you. +You can resolve this matter today by making a payment over the phone. +We accept payment through gift cards, wire transfer, or cryptocurrency. +Please provide your social security number so we can verify your identity. +Time is running out and you must act now to avoid criminal prosecution. +Do not hang up this call or your local police department will be notified. +Press one to be connected to a payment specialist immediately. diff --git a/demo/backend/sample_calls/legitimate_call.txt b/demo/backend/sample_calls/legitimate_call.txt new file mode 100644 index 0000000000000000000000000000000000000000..54c898f162b72c3d13733869bb3f1fb78bc59904 --- /dev/null +++ b/demo/backend/sample_calls/legitimate_call.txt @@ -0,0 +1,7 @@ +Hi, this is Sarah from Doctor Smith's office calling. +I am calling to confirm your appointment scheduled for next Tuesday at two thirty in the afternoon. +Doctor Smith will be performing your annual checkup. +Please remember to bring your insurance card and a photo ID. +Also, please arrive about fifteen minutes early to fill out any updated paperwork. +If you have any questions or need to reschedule, please call us back at your earliest convenience. +We look forward to seeing you next week. Have a great day! diff --git a/demo/backend/sample_calls/prize_notification_scam.txt b/demo/backend/sample_calls/prize_notification_scam.txt new file mode 100644 index 0000000000000000000000000000000000000000..91cbe0b682960a2a5e4ee1c96fc13be74cfeb4d1 --- /dev/null +++ b/demo/backend/sample_calls/prize_notification_scam.txt @@ -0,0 +1,12 @@ +Congratulations, you have been selected as our grand prize winner in the National Consumer Sweepstakes. +You have won a cash prize of two hundred and fifty thousand dollars and a brand new Mercedes Benz. +This is not a sales call, you have legitimately won and we just need to process your claim. +Your entry was automatically submitted when you made a purchase at a participating retailer last month. +To claim your prize, there is a small processing fee of just nine hundred and ninety five dollars. +This covers the taxes, insurance, and shipping costs for your new vehicle. +Once we receive the processing fee, your cash prize will be deposited directly into your bank account within 48 hours. +I will need your full name, date of birth, and bank account information to set up the deposit. +We also need your social security number for tax reporting purposes as required by federal law. +This offer expires today, so we need to complete the claim process on this call. +If you'd like, I can transfer you to our prize verification department to confirm your winnings. +Shall I go ahead and start the paperwork for your two hundred and fifty thousand dollar prize? diff --git a/demo/backend/sample_calls/tech_support_scam.txt b/demo/backend/sample_calls/tech_support_scam.txt new file mode 100644 index 0000000000000000000000000000000000000000..b1391fcd9cb9362ac744fe13b58133d86e8ec536 --- /dev/null +++ b/demo/backend/sample_calls/tech_support_scam.txt @@ -0,0 +1,11 @@ +Hello, this is the Microsoft Windows technical support department. +We have detected that your computer has been compromised by malicious software. +Hackers are currently accessing your personal files and banking information. +You need to act immediately to prevent further damage to your system. +I will need you to download our remote access tool so we can fix this problem. +Please go to this website and enter the access code I give you. +We need to verify your identity so can you please provide your email password. +There is a one-time security fee of two hundred and ninety nine dollars to clean the infection. +You can pay with a credit card or by purchasing Google Play gift cards at your nearest store. +If you do not resolve this right now your computer will be permanently locked by the hackers. +I urge you to stay on the line because this is extremely urgent. diff --git a/demo/backend/sample_calls/utility_shutoff_scam.txt b/demo/backend/sample_calls/utility_shutoff_scam.txt new file mode 100644 index 0000000000000000000000000000000000000000..af91a3eeec65fa5a79c01dc6920738dd6a402265 --- /dev/null +++ b/demo/backend/sample_calls/utility_shutoff_scam.txt @@ -0,0 +1,12 @@ +This is an urgent notice from your electric utility company's billing department. +Our records show that your account is severely past due with a balance of eight hundred and forty two dollars. +If payment is not received within the next two hours, your electricity will be disconnected. +This is your final warning before we dispatch a technician to your address to shut off service. +Once disconnected, there will be an additional reconnection fee of three hundred and fifty dollars. +We understand this may be unexpected, but our system shows multiple missed payments on your account. +To prevent disconnection, you can make an immediate payment over the phone right now. +We accept payment through prepaid debit cards, money orders, or cryptocurrency for immediate processing. +I will need your account number and the last four digits of your social security number to pull up your file. +If you make the payment now, I can personally ensure your service remains active. +Please note that our regular customer service line is closed, this is the only way to resolve this today. +Do you have a pen ready so I can give you the payment instructions? diff --git a/demo/frontend/index.html b/demo/frontend/index.html new file mode 100644 index 0000000000000000000000000000000000000000..be43759911819eb15e4baee458180cda77b5c6fe --- /dev/null +++ b/demo/frontend/index.html @@ -0,0 +1,16 @@ + + + + + + + SentinelEdge - Federated Edge AI Fraud Detection + + + + + +
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"less": "*", + "lightningcss": "^1.21.0", + "sass": "*", + "sass-embedded": "*", + "stylus": "*", + "sugarss": "*", + "terser": "^5.4.0" + }, + "peerDependenciesMeta": { + "@types/node": { + "optional": true + }, + "less": { + "optional": true + }, + "lightningcss": { + "optional": true + }, + "sass": { + "optional": true + }, + "sass-embedded": { + "optional": true + }, + "stylus": { + "optional": true + }, + "sugarss": { + "optional": true + }, + "terser": { + "optional": true + } + } + }, + "node_modules/yallist": { + "version": "3.1.1", + "resolved": "https://registry.npmjs.org/yallist/-/yallist-3.1.1.tgz", + "integrity": "sha512-a4UGQaWPH59mOXUYnAG2ewncQS4i4F43Tv3JoAM+s2VDAmS9NsK8GpDMLrCHPksFT7h3K6TOoUNn2pb7RoXx4g==", + "dev": true, + "license": "ISC" + } + } +} diff --git a/demo/frontend/package.json b/demo/frontend/package.json new file mode 100644 index 0000000000000000000000000000000000000000..15448ab3449fbbb923c3f0daae53bc32bbe0bee1 --- /dev/null +++ b/demo/frontend/package.json @@ -0,0 +1,27 @@ +{ + "name": "sentineledge-demo", + "private": true, + "version": "0.1.0", + "type": "module", + "scripts": { + "dev": "vite", + "build": "tsc && vite build", + "preview": "vite preview" + }, + "dependencies": { + "react": "^18.3.1", + "react-dom": "^18.3.1", + "recharts": "^2.12.0", + "lucide-react": "^0.344.0" + }, + "devDependencies": { + "@types/react": "^18.3.1", + "@types/react-dom": "^18.3.0", + "@vitejs/plugin-react": "^4.3.0", + "autoprefixer": "^10.4.19", + "postcss": "^8.4.38", + "tailwindcss": "^3.4.3", + "typescript": "^5.4.5", + "vite": "^5.2.11" + } +} diff --git a/demo/frontend/postcss.config.js b/demo/frontend/postcss.config.js new file mode 100644 index 0000000000000000000000000000000000000000..2e7af2b7f1a6f391da1631d93968a9d487ba977d --- /dev/null +++ b/demo/frontend/postcss.config.js @@ -0,0 +1,6 @@ +export default { + plugins: { + tailwindcss: {}, + autoprefixer: {}, + }, +} diff --git a/demo/frontend/public/favicon-16x16.svg b/demo/frontend/public/favicon-16x16.svg new file mode 100644 index 0000000000000000000000000000000000000000..c1499b82389ea32aa0687134eb24a1e8490eb794 --- /dev/null +++ b/demo/frontend/public/favicon-16x16.svg @@ -0,0 +1,19 @@ + + + + + + + + + + + + + + + + + diff --git a/demo/frontend/public/favicon-32x32.svg b/demo/frontend/public/favicon-32x32.svg new file mode 100644 index 0000000000000000000000000000000000000000..2b6f99a36ecb4ab43f1c0aa8b5ea6c65d154a9e9 --- /dev/null +++ b/demo/frontend/public/favicon-32x32.svg @@ -0,0 +1,30 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/demo/frontend/public/sentineledge-logo-dark.svg b/demo/frontend/public/sentineledge-logo-dark.svg new file mode 100644 index 0000000000000000000000000000000000000000..a1dec3595d518e30368cfe2b6255c9e241038388 --- /dev/null +++ b/demo/frontend/public/sentineledge-logo-dark.svg @@ -0,0 +1,81 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + SENTINEL + + + EDGE + + + + + + AI-POWERED SCAM DETECTION + diff --git a/demo/frontend/src/App.tsx b/demo/frontend/src/App.tsx new file mode 100644 index 0000000000000000000000000000000000000000..dbad90de1b3d742e7017850853ad6a7ebabb53e7 --- /dev/null +++ b/demo/frontend/src/App.tsx @@ -0,0 +1,619 @@ +import { useState, useCallback, useRef, useEffect } from 'react' +import { Shield, Radio, Lock, GitBranch, Activity } from 'lucide-react' +import PhoneSimulator from './components/PhoneSimulator' +import CallScreen from './components/CallScreen' +import TranscriptPanel from './components/TranscriptPanel' +import ScoreGauge from './components/ScoreGauge' +import FeatureBreakdown from './components/FeatureBreakdown' +import DemoControls from './components/DemoControls' +import PrivacyDemo from './components/PrivacyDemo' +import FederatedDashboard from './components/FederatedDashboard' +import CallHistory from './components/CallHistory' +import type { CallHistoryEntry } from './components/CallHistory' +import { useWebSocket } from './hooks/useWebSocket' + +// -------- Sample call scripts -------- +interface ScriptLine { + text: string + score: number + features: Record +} + +const SAMPLE_CALLS: Record = { + irs_scam: { + description: 'IRS Scam Call', + lines: [ + { text: "Hello, this is Officer James Wilson from the Internal Revenue Service.", score: 0.15, features: { urgency: 0.1, financial_terms: 0.2, authority_claim: 0.3 } }, + { text: "We have detected a serious issue with your tax filing for the previous year.", score: 0.25, features: { urgency: 0.2, financial_terms: 0.3, authority_claim: 0.3, threat_language: 0.1 } }, + { text: "There is an outstanding balance of $4,789 that must be resolved immediately.", score: 0.42, features: { urgency: 0.5, financial_terms: 0.7, specific_amount: 0.6, time_pressure: 0.4 } }, + { text: "If this is not paid today, a warrant will be issued for your arrest.", score: 0.68, features: { urgency: 0.9, threat_language: 0.8, time_pressure: 0.9, authority_claim: 0.5 } }, + { text: "You need to purchase Google Play gift cards worth $4,789 and read me the codes.", score: 0.85, features: { urgency: 0.8, financial_terms: 0.9, gift_card_mention: 1.0, unusual_payment: 1.0 } }, + { text: "This is the only way to avoid criminal prosecution. Do it now.", score: 0.92, features: { urgency: 1.0, threat_language: 0.95, time_pressure: 1.0, coercion: 0.9, unusual_payment: 0.8 } }, + { text: "Are you at the store yet? You need to hurry, the deadline is in one hour.", score: 0.95, features: { urgency: 1.0, time_pressure: 1.0, coercion: 0.85, unusual_payment: 0.7 } }, + ], + }, + tech_support: { + description: 'Tech Support Scam', + lines: [ + { text: "Hi, this is Mike from Microsoft Technical Support department.", score: 0.12, features: { authority_claim: 0.3, tech_jargon: 0.1 } }, + { text: "We have detected that your computer has been compromised by a virus.", score: 0.28, features: { urgency: 0.3, tech_jargon: 0.4, fear_induction: 0.3 } }, + { text: "Hackers are currently stealing your personal information as we speak.", score: 0.45, features: { urgency: 0.6, fear_induction: 0.7, time_pressure: 0.5 } }, + { text: "I need you to download a remote access tool so I can fix this for you.", score: 0.62, features: { urgency: 0.5, remote_access: 0.9, tech_jargon: 0.5, trust_request: 0.6 } }, + { text: "Please go to this website and enter your computer password when prompted.", score: 0.78, features: { credential_request: 0.95, remote_access: 0.8, trust_request: 0.7 } }, + { text: "Now I also need your bank details to process the security deposit of $299.", score: 0.91, features: { financial_terms: 0.9, credential_request: 0.8, specific_amount: 0.7, trust_request: 0.6 } }, + ], + }, + bank_fraud: { + description: 'Bank Fraud Call', + lines: [ + { text: "Good afternoon, this is the fraud prevention department at First National Bank.", score: 0.10, features: { authority_claim: 0.3, financial_terms: 0.2 } }, + { text: "We noticed some suspicious activity on your account ending in 4821.", score: 0.22, features: { authority_claim: 0.3, financial_terms: 0.4, specific_detail: 0.3 } }, + { text: "Someone attempted to make a purchase of $2,340 at an electronics store.", score: 0.30, features: { financial_terms: 0.5, urgency: 0.3, specific_amount: 0.4 } }, + { text: "To verify your identity, I need you to confirm your full Social Security number.", score: 0.65, features: { credential_request: 0.95, authority_claim: 0.4, pii_request: 0.9 } }, + { text: "I also need your online banking password and the PIN for your debit card.", score: 0.88, features: { credential_request: 1.0, pii_request: 1.0, financial_terms: 0.7 } }, + { text: "Please also share the verification code that was just sent to your phone.", score: 0.93, features: { credential_request: 0.9, mfa_bypass: 1.0, urgency: 0.6 } }, + ], + }, + legitimate: { + description: 'Legitimate Call', + lines: [ + { text: "Hi, this is Sarah from Dr. Thompson's office calling about your appointment.", score: 0.03, features: { authority_claim: 0.05 } }, + { text: "We have you scheduled for next Tuesday at 2:30 PM for your annual checkup.", score: 0.02, features: {} }, + { text: "I wanted to confirm that the time still works for you.", score: 0.01, features: {} }, + { text: "Also, please remember to bring your insurance card and a photo ID.", score: 0.04, features: { pii_request: 0.05 } }, + { text: "If you need to reschedule, you can call us back at the number on our website.", score: 0.02, features: {} }, + { text: "Thank you, and we look forward to seeing you next week. Have a great day!", score: 0.01, features: {} }, + ], + }, +} + +const AVAILABLE_CALLS = Object.entries(SAMPLE_CALLS).map(([id, call]) => ({ + id, + description: call.description, +})) + +type AlertLevel = 'safe' | 'low' | 'medium' | 'high' | 'critical' + +function getAlertLevel(score: number): AlertLevel { + if (score < 0.15) return 'safe' + if (score < 0.3) return 'low' + if (score < 0.5) return 'medium' + if (score < 0.75) return 'high' + return 'critical' +} + +type Tab = 'detection' | 'privacy' | 'federated' + +interface AudioDevice { + index: number + name: string + max_input_channels: number + default_samplerate: number +} + +export default function App() { + const [activeTab, setActiveTab] = useState('detection') + const [isCallActive, setIsCallActive] = useState(false) + const [isMicActive, setIsMicActive] = useState(true) + const [callDuration, setCallDuration] = useState(0) + const [currentCallId, setCurrentCallId] = useState(null) + const [sentences, setSentences] = useState>([]) + const [currentScore, setCurrentScore] = useState(0) + const [emaScore, setEmaScore] = useState(0) + const [features, setFeatures] = useState>({}) + const [alertDismissed, setAlertDismissed] = useState(false) + const [fraudSignalReceived, setFraudSignalReceived] = useState(false) + const [privacySentences, setPrivacySentences] = useState }>>([]) + const [gradientVectors, setGradientVectors] = useState([]) + const [isBackendDriven, setIsBackendDriven] = useState(false) + const [audioDevices, setAudioDevices] = useState([]) + const [selectedInputDevice, setSelectedInputDevice] = useState('') + const [callHistory, setCallHistory] = useState([]) + + const durationRef = useRef | null>(null) + + const speakCallerLine = useCallback((text: string) => { + if (!('speechSynthesis' in window)) { + return + } + + // Keep caller speech readable and avoid queued overlap. + window.speechSynthesis.cancel() + const utterance = new SpeechSynthesisUtterance(text) + utterance.rate = 0.95 + utterance.pitch = 0.95 + window.speechSynthesis.speak(utterance) + }, []) + + // WebSocket hook for live backend connection + const wsUrl = currentCallId + ? (() => { + const params = new URLSearchParams() + const isScriptedCall = currentCallId !== 'live_mic' + if (isScriptedCall || isMicActive) { + params.set('interactive', '1') + } + if (selectedInputDevice) { + params.set('input_device', selectedInputDevice) + } + const qs = params.toString() + const wsBase = import.meta.env.DEV + ? 'ws://localhost:8000' + : `${window.location.protocol === 'https:' ? 'wss:' : 'ws:'}//${window.location.host}` + return `${wsBase}/ws/call/${currentCallId}${qs ? `?${qs}` : ''}` + })() + : '' + + const { connect, disconnect, send, isConnected } = useWebSocket({ + url: wsUrl, + onMessage: (data) => { + if (data.type === 'call_start') { + setIsBackendDriven(true) + return + } + + if (data.type === 'sentence') { + const speaker = data.speaker === 'you' ? 'You' : 'Scammer' + const isUser = data.speaker === 'you' + const displayScore = isUser + ? (data.raw_score ?? 0) + : (data.ema_score ?? data.raw_score ?? 0) + const newSentence = { + text: `[${speaker}] ${data.text}`, + score: displayScore, + index: data.index ?? 0, + } + setSentences(prev => [...prev, newSentence]) + if (!isUser) { + setCurrentScore(data.raw_score ?? 0) + setEmaScore(data.ema_score ?? data.raw_score ?? 0) + if (data.features) setFeatures(data.features) + } + setPrivacySentences(prev => [ + ...prev, + { + text: data.text, + score: displayScore, + features: data.features ?? {}, + }, + ]) + + // In scripted call mode, play the caller sentence aloud. + if (data.speaker !== 'you' && currentCallId !== 'live_mic') { + speakCallerLine(data.text) + } + return + } + + if (data.type === 'fraud_detected') { + setFraudSignalReceived(true) + setAlertDismissed(false) + if (typeof data.ema_score === 'number') { + setEmaScore(data.ema_score) + } + if (Array.isArray(data.reasons) && data.reasons.length > 0) { + const mapped: Record = {} + data.reasons.forEach((reason: string, idx: number) => { + mapped[`reason_${idx + 1}_${reason.toLowerCase().replace(/[^a-z0-9]+/g, '_')}`] = 1 + }) + setFeatures(mapped) + } + return + } + + if (data.type === 'waiting_for_reply') { + setSentences(prev => [ + ...prev, + { + text: `[System] Your turn: speak now (window ${data.timeout_seconds ?? 15}s)`, + score: 0, + index: prev.length, + }, + ]) + return + } + + if (data.type === 'user_timeout') { + setSentences(prev => [ + ...prev, + { + text: '[System] No reply detected, continuing call flow.', + score: 0, + index: prev.length, + }, + ]) + return + } + + if (data.type === 'user_echo_detected') { + setSentences(prev => [ + ...prev, + { + text: `[System] ${data.message}`, + score: 0, + index: prev.length, + }, + ]) + return + } + + if (data.type === 'call_end' || data.type === 'call_blocked') { + setIsCallActive(false) + setCurrentCallId(null) + setFraudSignalReceived(false) + if (durationRef.current) { + clearInterval(durationRef.current) + durationRef.current = null + } + if ('speechSynthesis' in window) { + window.speechSynthesis.cancel() + } + return + } + + if (data.type === 'error') { + console.error('Backend error:', data.message) + setSentences(prev => [ + ...prev, + { + text: `[System] ${data.message}`, + score: 0, + index: prev.length, + }, + ]) + } + }, + onOpen: () => console.log('WebSocket connected'), + onClose: () => { + console.log('WebSocket disconnected') + setIsBackendDriven(false) + }, + autoConnect: false, + }) + + const startCall = useCallback((callId: string) => { + if (!SAMPLE_CALLS[callId]) return + + // Reset state + setSentences([]) + setCurrentScore(0) + setEmaScore(0) + setFeatures({}) + setAlertDismissed(false) + setFraudSignalReceived(false) + setCallDuration(0) + setCurrentCallId(callId) + setIsCallActive(true) + setPrivacySentences([]) + setGradientVectors([]) + setIsBackendDriven(false) + + // Start duration timer + if (durationRef.current) { + clearInterval(durationRef.current) + } + durationRef.current = setInterval(() => { + setCallDuration(prev => prev + 1) + }, 1000) + }, []) + + useEffect(() => { + const loadAudioDevices = async () => { + try { + const apiBase = import.meta.env.DEV ? 'http://localhost:8000' : '' + const resp = await fetch(`${apiBase}/api/audio-devices`) + const data = await resp.json() + const devices: AudioDevice[] = Array.isArray(data.devices) ? data.devices : [] + setAudioDevices(devices) + if (devices.length > 0) { + setSelectedInputDevice(String(devices[0].index)) + } + } catch { + setAudioDevices([]) + } + } + + void loadAudioDevices() + }, []) + + const localPlaybackRef = useRef[]>([]) + + useEffect(() => { + if (!isCallActive || !currentCallId) return + + // Try WebSocket first + connect() + + // After a short delay, check if backend connected. + // If not, run local playback with built-in scripts. + const fallbackTimer = setTimeout(() => { + if (!isBackendDriven && currentCallId) { + const call = SAMPLE_CALLS[currentCallId] + if (!call) return + + let ema = 0 + const alpha = 0.3 + + call.lines.forEach((line, i) => { + const timer = setTimeout(() => { + ema = i === 0 ? line.score : alpha * line.score + (1 - alpha) * ema + setSentences(prev => [...prev, { text: line.text, score: line.score, index: i }]) + setCurrentScore(line.score) + setEmaScore(ema) + setFeatures(line.features) + setPrivacySentences(prev => [...prev, { text: line.text, score: line.score, features: line.features }]) + if (ema >= 0.5) setFraudSignalReceived(true) + }, (i + 1) * 3000) + localPlaybackRef.current.push(timer) + }) + } + }, 2000) + + return () => { + clearTimeout(fallbackTimer) + localPlaybackRef.current.forEach(t => clearTimeout(t)) + localPlaybackRef.current = [] + } + }, [isCallActive, currentCallId, connect, isBackendDriven]) + + // Record a completed call into history + const recordCallHistory = useCallback((outcome: 'Blocked' | 'Dismissed' | 'Completed') => { + if (!currentCallId) return + const call = SAMPLE_CALLS[currentCallId] + if (!call) return + + // Gather top features from all sentences seen so far + const featureCounts: Record = {} + privacySentences.forEach(s => { + Object.entries(s.features).forEach(([k, v]) => { + if (v > 0.3) { + featureCounts[k] = Math.max(featureCounts[k] ?? 0, v) + } + }) + }) + const topFeatures = Object.entries(featureCounts) + .sort(([, a], [, b]) => b - a) + .slice(0, 5) + .map(([k]) => k) + + const peakScore = sentences.reduce((max, s) => Math.max(max, s.score), 0) + + const entry: CallHistoryEntry = { + id: `${currentCallId}_${Date.now()}`, + callType: currentCallId, + callLabel: call.description, + duration: callDuration, + peakScore, + finalScore: emaScore, + outcome, + timestamp: Date.now(), + totalSentences: sentences.length, + topFeatures, + } + setCallHistory(prev => [entry, ...prev]) + }, [currentCallId, callDuration, emaScore, sentences, privacySentences]) + + const endCall = useCallback(() => { + recordCallHistory('Completed') + setIsCallActive(false) + setCurrentCallId(null) + if (durationRef.current) { + clearInterval(durationRef.current) + durationRef.current = null + } + if ('speechSynthesis' in window) { + window.speechSynthesis.cancel() + } + disconnect() + }, [disconnect, recordCallHistory]) + + const blockCaller = useCallback(() => { + send({ action: 'block' }) + endCall() + }, [endCall, send]) + + const dismissAlert = useCallback(() => { + send({ action: 'dismiss' }) + setAlertDismissed(true) + }, [send]) + + // Cleanup on unmount + useEffect(() => { + return () => { + if (durationRef.current) clearInterval(durationRef.current) + if ('speechSynthesis' in window) { + window.speechSynthesis.cancel() + } + } + }, []) + + const alertLevel = getAlertLevel(emaScore) + const effectiveAlertLevel = alertDismissed || !fraudSignalReceived ? 'safe' : alertLevel + const callerNames: Record = { + live_mic: 'Live Microphone', + irs_scam: 'Officer James Wilson', + tech_support: 'Mike - Microsoft', + bank_fraud: 'First National Bank', + legitimate: "Dr. Thompson's Office", + } + const callerNumbers: Record = { + live_mic: 'On-device audio stream', + irs_scam: '+1 (202) 555-0147', + tech_support: '+1 (800) 555-0199', + bank_fraud: '+1 (312) 555-0183', + legitimate: '+1 (415) 555-0126', + } + + const tabs: { id: Tab; label: string; icon: React.ReactNode }[] = [ + { id: 'detection', label: 'Live Detection', icon: }, + { id: 'privacy', label: 'Privacy Demo', icon: }, + { id: 'federated', label: 'Federated Learning', icon: }, + ] + const isDetectionLoading = isCallActive && sentences.length === 0 + + return ( +
+ {/* Header */} +
+
+
+
+ +
+
+
+

+ Sentinel + Edge +

+

+ Federated Edge AI Fraud Detection +

+
+
+ + {/* Tabs */} + + + {/* Status indicator */} +
+ + + {isConnected ? 'Backend Connected' : isBackendDriven ? 'Reconnecting...' : 'Backend Disconnected'} + +
+
+
+ + {/* Main Content */} +
+ {activeTab === 'detection' && ( +
+ {/* Demo Controls */} + setIsMicActive(!isMicActive)} + onSelectMicDevice={setSelectedInputDevice} + isCallActive={isCallActive} + isMicActive={isMicActive} + selectedMicDevice={selectedInputDevice} + micDevices={audioDevices.map(d => ({ value: String(d.index), label: `${d.index}: ${d.name}` }))} + availableCalls={AVAILABLE_CALLS} + /> + + {/* Main detection layout */} +
+ {/* Phone Simulator */} +
+ + v > 0.5) + .sort(([, a], [, b]) => b - a) + .map(([k]) => k.replace(/_/g, ' ')) + } + fraudScore={emaScore} + /> + +
+ + {/* Right side panels */} +
+ {/* Score + Features row */} +
+
+ {isDetectionLoading ? ( +
+
+
+
+
+ ) : ( + + )} +
+
+ {isDetectionLoading ? ( +
+ {[0, 1, 2, 3].map(item => ( +
+
+
+
+
+
+
+ ))} +
+ ) : ( + + )} +
+
+ + {/* Transcript */} +
+ {isDetectionLoading ? ( +
+ {[0, 1, 2].map(item => ( +
+
+
+
+
+ ))} +
+ ) : ( + + )} +
+
+
+ + {/* Call History - collapsible section below main content */} + +
+ )} + + {activeTab === 'privacy' && ( + + )} + + {activeTab === 'federated' && ( + + )} +
+
+ ) +} diff --git a/demo/frontend/src/components/CallHistory.tsx b/demo/frontend/src/components/CallHistory.tsx new file mode 100644 index 0000000000000000000000000000000000000000..c4f457db72dce1769c1860ee386704e275bc414b --- /dev/null +++ b/demo/frontend/src/components/CallHistory.tsx @@ -0,0 +1,240 @@ +import { useState } from 'react' +import { + ChevronDown, + ChevronUp, + Phone, + AlertTriangle, + Shield, + Clock, +} from 'lucide-react' + +// -------- Types -------- +export interface CallHistoryEntry { + id: string + callType: string + callLabel: string + duration: number // seconds + peakScore: number + finalScore: number + outcome: 'Blocked' | 'Dismissed' | 'Completed' + timestamp: number // Date.now() + totalSentences: number + topFeatures: string[] +} + +interface CallHistoryProps { + entries: CallHistoryEntry[] +} + +// -------- Helpers -------- +function formatDuration(seconds: number): string { + const mins = Math.floor(seconds / 60) + const secs = seconds % 60 + return `${mins.toString().padStart(2, '0')}:${secs.toString().padStart(2, '0')}` +} + +function relativeTime(timestamp: number): string { + const diff = Math.floor((Date.now() - timestamp) / 1000) + if (diff < 5) return 'just now' + if (diff < 60) return `${diff}s ago` + const mins = Math.floor(diff / 60) + if (mins < 60) return `${mins} minute${mins !== 1 ? 's' : ''} ago` + const hours = Math.floor(mins / 60) + return `${hours} hour${hours !== 1 ? 's' : ''} ago` +} + +function getOutcomeStyle(outcome: string): { bg: string; text: string } { + switch (outcome) { + case 'Blocked': + return { bg: 'bg-alert/10', text: 'text-alert' } + case 'Dismissed': + return { bg: 'bg-warning/10', text: 'text-warning' } + case 'Completed': + default: + return { bg: 'bg-safe/10', text: 'text-safe' } + } +} + +function getScoreColor(score: number): string { + if (score < 0.3) return 'text-safe' + if (score < 0.5) return 'text-warning' + if (score < 0.75) return 'text-orange-400' + return 'text-alert' +} + +function getScoreBgColor(score: number): string { + if (score < 0.3) return 'bg-safe/10' + if (score < 0.5) return 'bg-warning/10' + if (score < 0.75) return 'bg-orange-500/10' + return 'bg-alert/10' +} + +function getCallIcon(callType: string) { + switch (callType) { + case 'irs_scam': + case 'tech_support': + case 'bank_fraud': + return + case 'legitimate': + return + default: + return + } +} + +function getCallIconColor(callType: string): string { + switch (callType) { + case 'irs_scam': + return 'text-alert bg-alert/10' + case 'tech_support': + return 'text-orange-400 bg-orange-500/10' + case 'bank_fraud': + return 'text-warning bg-warning/10' + case 'legitimate': + return 'text-safe bg-safe/10' + default: + return 'text-gray-400 bg-gray-700/30' + } +} + +// -------- Component -------- +export default function CallHistory({ entries }: CallHistoryProps) { + const [isExpanded, setIsExpanded] = useState(true) + const [selectedId, setSelectedId] = useState(null) + + const selectedEntry = entries.find(e => e.id === selectedId) ?? null + + return ( +
+ {/* Collapsible header */} + + + {/* Body */} + {isExpanded && ( +
+ {entries.length === 0 ? ( + /* Empty state */ +
+ +

No calls yet.

+

Start a sample call to see results here.

+
+ ) : ( +
+ {entries.map((entry) => ( +
+ {/* Row */} + + + {/* Expanded detail */} + {selectedId === entry.id && ( +
+
+ {/* Summary row */} +
+
+

Total Sentences

+

{entry.totalSentences}

+
+
+

Peak Score

+

+ {(entry.peakScore * 100).toFixed(1)}% +

+
+
+

Final Score

+

+ {(entry.finalScore * 100).toFixed(1)}% +

+
+
+ + {/* Top features */} + {entry.topFeatures.length > 0 && ( +
+

Top Features

+
+ {entry.topFeatures.map((feature) => ( + + {feature.replace(/_/g, ' ')} + + ))} +
+
+ )} +
+
+ )} +
+ ))} +
+ )} +
+ )} +
+ ) +} diff --git a/demo/frontend/src/components/CallScreen.tsx b/demo/frontend/src/components/CallScreen.tsx new file mode 100644 index 0000000000000000000000000000000000000000..f40a7e019de6e859260048114f45839aa6b10710 --- /dev/null +++ b/demo/frontend/src/components/CallScreen.tsx @@ -0,0 +1,238 @@ +import { useEffect, useState } from 'react' +import { + Phone, + PhoneOff, + Mic, + MicOff, + Volume2, + Grid3X3, + UserCircle2, + Wifi, + Battery, + Signal, + Camera, +} from 'lucide-react' +import FraudAlert from './FraudAlert' + +interface CallScreenProps { + callerName: string + callerNumber: string + duration: number + isActive: boolean + onEndCall: () => void + onBlock: () => void + onDismissAlert: () => void + alertLevel: 'safe' | 'low' | 'medium' | 'high' | 'critical' + alertReasons: string[] + fraudScore: number +} + +function formatDuration(seconds: number): string { + const mins = Math.floor(seconds / 60) + const secs = seconds % 60 + return `${mins.toString().padStart(2, '0')}:${secs.toString().padStart(2, '0')}` +} + +function getCurrentTime(): string { + const now = new Date() + return now.toLocaleTimeString('en-US', { hour: 'numeric', minute: '2-digit', hour12: true }) +} + +export default function CallScreen({ + callerName, + callerNumber, + duration, + isActive, + onEndCall, + onBlock, + onDismissAlert, + alertLevel, + alertReasons, + fraudScore, +}: CallScreenProps) { + const [isMuted, setIsMuted] = useState(false) + const [isSpeaker, setIsSpeaker] = useState(false) + const [isScreenShaking, setIsScreenShaking] = useState(false) + + const showAlert = isActive && (alertLevel === 'high' || alertLevel === 'critical' || alertLevel === 'medium') + const callerInitials = callerName + .split(' ') + .filter(Boolean) + .slice(0, 2) + .map(part => part[0]?.toUpperCase()) + .join('') + + useEffect(() => { + if (!showAlert) return + + setIsScreenShaking(true) + const timeout = window.setTimeout(() => { + setIsScreenShaking(false) + }, 480) + + return () => window.clearTimeout(timeout) + }, [showAlert, alertLevel]) + + return ( +
+ {/* Status bar */} +
+ {getCurrentTime()} +
+
+ + + +
+
+ + {/* Fraud Alert Overlay */} + {showAlert && ( +
+ +
+ )} + + {/* Main call content */} +
+ {isActive ? ( + <> + {/* Caller avatar */} +
+
+
+
+
+
+
+
+ {callerInitials || 'CP'} +
+
+ +
+ +
+ + {/* SentinelEdge protection badge */} +
+
+
+
+ + {/* Caller info */} +

{callerName}

+

{callerNumber}

+ + {/* Call timer */} +
+
+ + {formatDuration(duration)} + +
+ + {/* SentinelEdge status line */} +
+

+ + SentinelEdge Active +

+
+ + {callerName === 'Live Microphone' && ( +
+

+ Speak now: live mic analysis in progress +

+
+ )} + + ) : ( + /* Idle state */ +
+
+ +
+

No Active Call

+

Select a sample call to begin

+
+ )} +
+ + {/* Bottom action buttons */} + {isActive && ( +
+ {/* Action row */} +
+ + + + + + + +
+ + {/* End call button */} +
+ +
+
+ )} +
+ ) +} diff --git a/demo/frontend/src/components/DemoControls.tsx b/demo/frontend/src/components/DemoControls.tsx new file mode 100644 index 0000000000000000000000000000000000000000..09155d10554e96992c3d7ba3b766520f886c0b6c --- /dev/null +++ b/demo/frontend/src/components/DemoControls.tsx @@ -0,0 +1,143 @@ +import { Play, Mic, MicOff, AlertTriangle, Phone, Shield } from 'lucide-react' + +interface DemoControlsProps { + onSelectCall: (callId: string) => void + onToggleMic: () => void + onSelectMicDevice: (deviceValue: string) => void + isCallActive: boolean + isMicActive: boolean + selectedMicDevice: string + micDevices: Array<{ value: string; label: string }> + availableCalls: Array<{ id: string; description: string }> +} + +const callIcons: Record = { + irs_scam: { + icon: , + color: 'hover:bg-alert/10 hover:text-alert hover:border-alert/30', + }, + tech_support: { + icon: , + color: 'hover:bg-orange-500/10 hover:text-orange-400 hover:border-orange-500/30', + }, + bank_fraud: { + icon: , + color: 'hover:bg-warning/10 hover:text-warning hover:border-warning/30', + }, + legitimate: { + icon: , + color: 'hover:bg-safe/10 hover:text-safe hover:border-safe/30', + }, +} + +export default function DemoControls({ + onSelectCall, + onToggleMic, + onSelectMicDevice, + isCallActive, + isMicActive, + selectedMicDevice, + micDevices, + availableCalls, +}: DemoControlsProps) { + return ( +
+
+ {/* Left: sample calls */} +
+
+ + + Sample Calls + +
+
+ {availableCalls.map((call) => { + const config = callIcons[call.id] || { + icon: , + color: 'hover:bg-brand-teal/10 hover:text-brand-teal hover:border-brand-teal/30', + } + return ( + + ) + })} +
+
+ + {/* Right: mic toggle */} +
+
+
+ + + {!isCallActive && ( + <> + + {isMicActive + ? 'Mic is on for your side of role-play' + : 'Scripted caller mode: backend plays sample scam call lines'} + + + + + )} +
+ + {/* Status indicator */} + {isCallActive && ( +
+
+ Call Active +
+ )} +
+
+
+ ) +} diff --git a/demo/frontend/src/components/FeatureBreakdown.tsx b/demo/frontend/src/components/FeatureBreakdown.tsx new file mode 100644 index 0000000000000000000000000000000000000000..cf133b58c909273c1d95d84c56aaffa16a8e55bf --- /dev/null +++ b/demo/frontend/src/components/FeatureBreakdown.tsx @@ -0,0 +1,165 @@ +import { BarChart3, Info } from 'lucide-react' +import type { CSSProperties } from 'react' + +interface FeatureBreakdownProps { + features: Record +} + +const FEATURE_EXPLANATIONS: Record = { + authority_claim: 'The caller is presenting themselves as a trusted institution or official authority.', + coercion: 'Language is pressuring the recipient into acting before they can think or verify.', + credential_request: 'The conversation is attempting to obtain passwords, PINs, codes, or other secrets.', + fear_induction: 'The caller is using fear to reduce skepticism and speed up compliance.', + financial_terms: 'Money, payments, balances, or banking language is becoming central to the call.', + gift_card_mention: 'Requesting gift cards is a strong scam indicator because they are hard to trace or reverse.', + mfa_bypass: 'The caller is trying to capture one-time codes or verification steps meant to protect the user.', + pii_request: 'The caller is asking for sensitive personal information that should not be shared casually.', + remote_access: 'The caller is steering the user toward remote-control tools or device access.', + specific_amount: 'Using a precise dollar amount can make a scam feel more legitimate and urgent.', + specific_detail: 'Specific details are being used to create a false sense of credibility.', + tech_jargon: 'Technical language may be used to intimidate or confuse the recipient.', + threat_language: 'Threats of punishment, loss, or legal action are a major fraud signal.', + time_pressure: 'The caller is creating a countdown or deadline to force a rushed decision.', + trust_request: 'The caller is explicitly pushing the recipient to trust them or follow instructions without verification.', + unusual_payment: 'The requested payment method is uncommon for legitimate businesses or institutions.', + urgency: 'The tone suggests immediate action is required, which is common in scam escalation.', +} + +function getSeverity(value: number) { + if (value < 0.3) { + return { + label: 'Low', + color: '#14B8A6', + glow: 'rgba(20, 184, 166, 0.16)', + track: 'rgba(20, 184, 166, 0.12)', + } + } + if (value < 0.5) { + return { + label: 'Medium', + color: '#FBBF24', + glow: 'rgba(251, 191, 36, 0.18)', + track: 'rgba(251, 191, 36, 0.12)', + } + } + if (value < 0.75) { + return { + label: 'High', + color: '#FB923C', + glow: 'rgba(251, 146, 60, 0.2)', + track: 'rgba(251, 146, 60, 0.12)', + } + } + return { + label: 'Critical', + color: '#F87171', + glow: 'rgba(248, 113, 113, 0.22)', + track: 'rgba(248, 113, 113, 0.12)', + } +} + +function formatFeatureName(name: string): string { + return name + .replace(/_/g, ' ') + .replace(/\b\w/g, c => c.toUpperCase()) +} + +export default function FeatureBreakdown({ features }: FeatureBreakdownProps) { + const sortedFeatures = Object.entries(features) + .filter(([, value]) => value > 0) + .sort(([, a], [, b]) => b - a) + .slice(0, 8) + + return ( +
+
+ +

Feature Breakdown

+
+ +
+ {sortedFeatures.length === 0 ? ( +
+
+ +

No features detected

+

Analysis data appears during calls

+
+
+ ) : ( + sortedFeatures.map(([name, value], index) => { + const severity = getSeverity(value) + const description = FEATURE_EXPLANATIONS[name] ?? 'This signal contributes to the overall fraud risk score.' + + return ( +
+
+
+ + {formatFeatureName(name)} + +
+ +
+

{formatFeatureName(name)}

+

{description}

+
+ + {severity.label} Signal +
+
+
+
+ + {(value * 100).toFixed(0)}% + +
+ +
+
+
+
+ ) + }) + )} +
+ + {sortedFeatures.length > 0 && ( +
+
+ + {sortedFeatures.length} active indicator{sortedFeatures.length !== 1 ? 's' : ''} + +
+ {[ + { label: 'Low', color: '#14B8A6' }, + { label: 'Med', color: '#FBBF24' }, + { label: 'High', color: '#FB923C' }, + { label: 'Crit', color: '#F87171' }, + ].map(item => ( +
+
+ {item.label} +
+ ))} +
+
+
+ )} +
+ ) +} diff --git a/demo/frontend/src/components/FederatedDashboard.tsx b/demo/frontend/src/components/FederatedDashboard.tsx new file mode 100644 index 0000000000000000000000000000000000000000..0d77b73f4fcd07d0f309646f20e883af4ab585e8 --- /dev/null +++ b/demo/frontend/src/components/FederatedDashboard.tsx @@ -0,0 +1,587 @@ +import { useState, useCallback, useEffect, useRef } from 'react' +import { + BarChart, + Bar, + XAxis, + YAxis, + CartesianGrid, + Tooltip, + ResponsiveContainer, + Legend, + Area, + AreaChart, +} from 'recharts' +import { + GitBranch, + Cpu, + Shield, + Lock, + Play, + RefreshCw, + TrendingUp, + Users, + Database, + Smartphone, + WifiOff, +} from 'lucide-react' + +// -------- Mock data generators (used as fallback when hub is offline) -------- +function generateRoundData(numRounds: number) { + const data = [] + let accuracy = 0.62 + let f1 = 0.55 + let loss = 0.82 + + for (let i = 1; i <= numRounds; i++) { + const accGain = (0.98 - accuracy) * 0.08 + (Math.random() - 0.5) * 0.015 + const f1Gain = (0.95 - f1) * 0.09 + (Math.random() - 0.5) * 0.02 + const lossDecay = loss * 0.06 + (Math.random() - 0.5) * 0.02 + + accuracy = Math.min(0.98, accuracy + accGain) + f1 = Math.min(0.95, f1 + f1Gain) + loss = Math.max(0.05, loss - lossDecay) + + data.push({ + round: i, + accuracy: Number(accuracy.toFixed(4)), + f1: Number(f1.toFixed(4)), + loss: Number(loss.toFixed(4)), + }) + } + return data +} + +function generateDeviceData() { + const devices = [ + { name: 'Device A', samples: 342, contribution: 0.23 }, + { name: 'Device B', samples: 287, contribution: 0.19 }, + { name: 'Device C', samples: 198, contribution: 0.14 }, + { name: 'Device D', samples: 456, contribution: 0.31 }, + { name: 'Device E', samples: 167, contribution: 0.13 }, + ] + return devices +} + +// Generate a random gradient delta for simulated FL submissions +function generateGradientDelta(size = 64): number[] { + return Array.from({ length: size }, () => + Number(((Math.random() - 0.5) * 0.01).toFixed(6)) + ) +} + +const CustomTooltip = ({ active, payload, label }: any) => { + if (active && payload && payload.length) { + return ( +
+

Round {label}

+ {payload.map((entry: any, idx: number) => ( +

+ {entry.name}: {(entry.value * 100).toFixed(1)}% +

+ ))} +
+ ) + } + return null +} + +export default function FederatedDashboard() { + const [roundData, setRoundData] = useState(() => generateRoundData(20)) + const [deviceData, setDeviceData] = useState(() => generateDeviceData()) + const [isSimulating, setIsSimulating] = useState(false) + const [currentRound, setCurrentRound] = useState(20) + const [hubConnected, setHubConnected] = useState(false) + const [totalSamples, setTotalSamples] = useState(1450) + const [privacyBudget, setPrivacyBudget] = useState('epsilon=1.0') + + const pollIntervalRef = useRef | null>(null) + const isMountedRef = useRef(true) + + // Fetch global metrics from the hub API + const fetchGlobalMetrics = useCallback(async () => { + try { + const response = await fetch(`${import.meta.env.DEV ? 'http://localhost:8080' : '/hub'}/v1/metrics/global`, { + signal: AbortSignal.timeout(3000), + }) + if (!response.ok) throw new Error(`HTTP ${response.status}`) + const data = await response.json() + + if (!isMountedRef.current) return + + // Map hub API response to our local state + if (data.accuracy_history && Array.isArray(data.accuracy_history)) { + const mapped = data.accuracy_history.map((entry: any, i: number) => ({ + round: entry.round ?? i + 1, + accuracy: entry.accuracy ?? 0, + f1: entry.f1 ?? 0, + loss: entry.loss ?? 0, + })) + if (mapped.length > 0) { + setRoundData(mapped) + setCurrentRound(mapped.length) + } + } + if (data.device_count != null) { + // Update device count visually -- keep existing device data shape + const count = Number(data.device_count) + if (count > 0 && count !== deviceData.length) { + const generated = Array.from({ length: count }, (_, i) => ({ + name: `Device ${String.fromCharCode(65 + i)}`, + samples: Math.floor(Math.random() * 400) + 100, + contribution: Number((1 / count).toFixed(2)), + })) + setDeviceData(generated) + } + } + if (data.round_count != null) { + setCurrentRound(Number(data.round_count)) + } + if (data.total_samples != null) { + setTotalSamples(Number(data.total_samples)) + } + if (data.privacy_budget != null) { + setPrivacyBudget(String(data.privacy_budget)) + } + + setHubConnected(true) + } catch { + if (isMountedRef.current) { + setHubConnected(false) + } + } + }, [deviceData.length]) + + // Fetch round status (current round progress) + const fetchRoundStatus = useCallback(async () => { + try { + const response = await fetch(`${import.meta.env.DEV ? 'http://localhost:8080' : '/hub'}/v1/round/status`, { + signal: AbortSignal.timeout(3000), + }) + if (!response.ok) throw new Error(`HTTP ${response.status}`) + const data = await response.json() + + if (!isMountedRef.current) return + + if (data.current_round != null) { + setCurrentRound(Number(data.current_round)) + } + if (data.is_aggregating != null) { + setIsSimulating(Boolean(data.is_aggregating)) + } + } catch { + // Silently fail -- global metrics fetch already handles connection status + } + }, []) + + // Poll hub API every 5 seconds while tab is active + useEffect(() => { + isMountedRef.current = true + + // Initial fetch + fetchGlobalMetrics() + fetchRoundStatus() + + const startPolling = () => { + if (pollIntervalRef.current) clearInterval(pollIntervalRef.current) + pollIntervalRef.current = setInterval(() => { + fetchGlobalMetrics() + fetchRoundStatus() + }, 5000) + } + + const stopPolling = () => { + if (pollIntervalRef.current) { + clearInterval(pollIntervalRef.current) + pollIntervalRef.current = null + } + } + + const handleVisibilityChange = () => { + if (document.hidden) { + stopPolling() + } else { + fetchGlobalMetrics() + fetchRoundStatus() + startPolling() + } + } + + startPolling() + document.addEventListener('visibilitychange', handleVisibilityChange) + + return () => { + isMountedRef.current = false + stopPolling() + document.removeEventListener('visibilitychange', handleVisibilityChange) + } + }, [fetchGlobalMetrics, fetchRoundStatus]) + + const latestMetrics = roundData[roundData.length - 1] + + // Run FL Round: POST to hub if connected, else simulate locally + const runSimulationRound = useCallback(async () => { + setIsSimulating(true) + + if (hubConnected) { + try { + const payload = { + device_id: `device_${Math.random().toString(36).slice(2, 8)}`, + gradient_delta: generateGradientDelta(), + num_samples: Math.floor(Math.random() * 200) + 50, + round: currentRound + 1, + } + const response = await fetch(`${import.meta.env.DEV ? 'http://localhost:8080' : '/hub'}/v1/federated/submit`, { + method: 'POST', + headers: { 'Content-Type': 'application/json' }, + body: JSON.stringify(payload), + signal: AbortSignal.timeout(5000), + }) + + if (response.ok) { + // After successful submit, re-fetch to pick up the new round data + await fetchGlobalMetrics() + await fetchRoundStatus() + setIsSimulating(false) + return + } + } catch { + // Hub became unreachable during the request -- fall through to local simulation + setHubConnected(false) + } + } + + // Fallback: local simulation + const timer = setTimeout(() => { + setRoundData(prev => { + const last = prev[prev.length - 1] + const newRound = { + round: last.round + 1, + accuracy: Number(Math.min(0.98, last.accuracy + (0.98 - last.accuracy) * 0.08 + (Math.random() - 0.5) * 0.01).toFixed(4)), + f1: Number(Math.min(0.95, last.f1 + (0.95 - last.f1) * 0.09 + (Math.random() - 0.5) * 0.015).toFixed(4)), + loss: Number(Math.max(0.05, last.loss - last.loss * 0.06 + (Math.random() - 0.5) * 0.01).toFixed(4)), + } + return [...prev, newRound] + }) + setCurrentRound(prev => prev + 1) + setIsSimulating(false) + }, 1500) + + return () => clearTimeout(timer) + }, [hubConnected, currentRound, fetchGlobalMetrics, fetchRoundStatus]) + + const stats = [ + { + label: 'Model Version', + value: `v${currentRound}`, + icon: , + color: 'text-brand-teal', + bgColor: 'bg-brand-teal/10', + }, + { + label: 'Active Devices', + value: String(deviceData.length), + icon: , + color: 'text-blue-400', + bgColor: 'bg-blue-400/10', + }, + { + label: 'Total Samples', + value: totalSamples.toLocaleString(), + icon: , + color: 'text-purple-400', + bgColor: 'bg-purple-400/10', + }, + { + label: 'Privacy Budget', + value: privacyBudget, + icon: , + color: 'text-safe', + bgColor: 'bg-safe/10', + }, + { + label: 'Accuracy', + value: `${(latestMetrics.accuracy * 100).toFixed(1)}%`, + icon: , + color: 'text-brand-teal', + bgColor: 'bg-brand-teal/10', + }, + { + label: 'F1 Score', + value: `${(latestMetrics.f1 * 100).toFixed(1)}%`, + icon: , + color: 'text-warning', + bgColor: 'bg-warning/10', + }, + ] + + return ( +
+ {/* Offline banner */} + {!hubConnected && ( +
+ +

+ Hub offline — showing simulated data +

+
+ )} + + {/* Header with action */} +
+
+
+ +
+
+
+

Federated Learning Dashboard

+ {/* Connection status indicator */} + +
+

Model training across distributed edge devices

+
+
+ +
+ + {/* Stats cards */} +
+ {stats.map((stat) => ( +
+
+
+ {stat.icon} +
+
+

{stat.value}

+

{stat.label}

+
+ ))} +
+ + {/* Charts */} +
+ {/* Accuracy & F1 over rounds */} +
+

+ + Model Performance Over Rounds +

+ + + + + + + + + + + + + + + `${(v * 100).toFixed(0)}%`} + /> + } /> + + + + + +
+ + {/* Loss over rounds */} +
+

+ + Training Loss +

+ + + + + + + + + + + v.toFixed(2)} + /> + } /> + + + +
+
+ + {/* Device contributions */} +
+

+ + Per-Device Sample Contributions +

+
+ + + + + + + + + + + {/* Device details table */} +
+ + + + + + + + + + + {deviceData.map((device) => ( + + + + + + + ))} + +
DeviceSamplesContributionStatus
{device.name}{device.samples} + {(device.contribution * 100).toFixed(0)}% + + +
+ Active + +
+
+
+
+ + {/* FL Process explanation */} +
+

How Federated Learning Works

+
+ {[ + { step: '1', title: 'Local Training', desc: 'Each device trains on its own call data', icon: , color: 'text-brand-teal' }, + { step: '2', title: 'Add DP Noise', desc: 'Differential privacy noise added to gradients', icon: , color: 'text-safe' }, + { step: '3', title: 'Aggregate', desc: 'Hub averages noisy gradients from all devices', icon: , color: 'text-warning' }, + { step: '4', title: 'Update Model', desc: 'Improved model pushed back to all devices', icon: , color: 'text-purple-400' }, + ].map((item) => ( +
+
+ {item.icon} +
+
Step {item.step}
+

{item.title}

+

{item.desc}

+
+ ))} +
+
+
+ ) +} diff --git a/demo/frontend/src/components/FraudAlert.tsx b/demo/frontend/src/components/FraudAlert.tsx new file mode 100644 index 0000000000000000000000000000000000000000..e0fcdda44f3cd09662b9f2fa2cf56fba516b06a2 --- /dev/null +++ b/demo/frontend/src/components/FraudAlert.tsx @@ -0,0 +1,144 @@ +import { ShieldAlert, AlertTriangle, Info, ShieldX } from 'lucide-react' + +interface FraudAlertProps { + riskLevel: 'medium' | 'high' | 'critical' + score: number + reasons: string[] + onBlock: () => void + onDismiss: () => void +} + +export default function FraudAlert({ + riskLevel, + score, + reasons, + onBlock, + onDismiss, +}: FraudAlertProps) { + const configs = { + critical: { + bg: 'bg-gradient-to-b from-alert/95 to-alert-dark/95', + border: 'border-alert-light', + icon: , + title: 'FRAUD DETECTED', + subtitle: 'This call exhibits strong fraud indicators', + containerClass: 'alert-overlay alert-overlay-critical', + badgeClass: 'bg-white/20 text-white', + }, + high: { + bg: 'bg-gradient-to-b from-warning/95 to-amber-700/95', + border: 'border-warning-light', + icon: , + title: 'HIGH RISK CALL', + subtitle: 'Suspicious patterns detected', + containerClass: 'alert-overlay', + badgeClass: 'bg-white/20 text-white', + }, + medium: { + bg: 'bg-gradient-to-b from-dark-card/98 to-dark-surface/98', + border: 'border-warning/50', + icon: , + title: 'Caution', + subtitle: 'Some suspicious indicators present', + containerClass: 'alert-banner', + badgeClass: 'bg-warning/20 text-warning', + }, + } + + const config = configs[riskLevel] + const percentage = Math.round(score * 100) + + if (riskLevel === 'medium') { + return ( +
+
+
+ {config.icon} +
+

{config.title}

+

{config.subtitle}

+
+
+ {percentage}% +
+
+ {reasons.length > 0 && ( +
+ {reasons.slice(0, 3).map((reason, i) => ( + + {reason} + + ))} +
+ )} +
+
+ ) + } + + // Full overlay for high/critical + return ( +
+
+ {/* Risk icon */} +
+
+ {config.icon} +
+ {riskLevel === 'critical' && ( +
+ )} +
+ + {/* Title */} +

+ {config.title} +

+

+ {config.subtitle} +

+ + {/* Confidence score */} +
+ {percentage}% Confidence +
+ + {/* Reasons */} + {reasons.length > 0 && ( +
+ {reasons.slice(0, 4).map((reason, i) => ( +
+
+ {reason} +
+ ))} +
+ )} + + {/* Action buttons */} +
+ + +
+ + {/* SentinelEdge branding */} +

+ Protected by SentinelEdge +

+
+
+ ) +} diff --git a/demo/frontend/src/components/PhoneSimulator.tsx b/demo/frontend/src/components/PhoneSimulator.tsx new file mode 100644 index 0000000000000000000000000000000000000000..61936441726902b8e770e31e2bcd091bfc300189 --- /dev/null +++ b/demo/frontend/src/components/PhoneSimulator.tsx @@ -0,0 +1,41 @@ +import { ReactNode } from 'react' + +interface PhoneSimulatorProps { + children: ReactNode +} + +export default function PhoneSimulator({ children }: PhoneSimulatorProps) { + return ( +
+
+
+
+ +
+
+
+
+
+ +
+
+ +
+
+
+
+
+ +
+ {children} +
+ +
+
+
+
+
+
+
+ ) +} diff --git a/demo/frontend/src/components/PrivacyDemo.tsx b/demo/frontend/src/components/PrivacyDemo.tsx new file mode 100644 index 0000000000000000000000000000000000000000..fefc4ebe193b209c095c3b576a9a70637993577d --- /dev/null +++ b/demo/frontend/src/components/PrivacyDemo.tsx @@ -0,0 +1,446 @@ +import { useState, useEffect, useRef, useCallback } from 'react' +import { Lock, Smartphone, Server, ArrowRight, Eye, EyeOff, ShieldCheck, RotateCcw, WifiOff, Wifi } from 'lucide-react' + +interface DisplayEntry { + text: string + score: number + features: Record + gradient: number[] + sigma: number + epsilon: number +} + +// -------- Fallback example data for offline mode -------- +const FALLBACK_EXAMPLES: DisplayEntry[] = [ + { + text: "This is Officer James Wilson from the Internal Revenue Service.", + score: 0.15, + features: { authority_claim: 0.3, financial_terms: 0.2 } as Record, + gradient: [-0.003421, 0.001872, -0.000543, 0.004219, -0.002103, 0.000891, -0.001567, 0.003245, 0.000123, -0.002876, 0.001432, -0.000765], + sigma: 0.5, + epsilon: 1.0, + }, + { + text: "There is an outstanding balance of $4,789 that must be resolved immediately.", + score: 0.42, + features: { urgency: 0.5, financial_terms: 0.7, specific_amount: 0.6, time_pressure: 0.4 } as Record, + gradient: [0.002156, -0.004312, 0.001098, -0.003567, 0.000432, -0.002789, 0.004101, -0.001234, 0.003456, -0.000876, 0.002345, -0.001678], + sigma: 0.5, + epsilon: 1.0, + }, + { + text: "If this is not paid today, a warrant will be issued for your arrest.", + score: 0.68, + features: { urgency: 0.9, threat_language: 0.8, time_pressure: 0.9, authority_claim: 0.5 } as Record, + gradient: [-0.001234, 0.003678, -0.002901, 0.000456, 0.004512, -0.003123, 0.001789, -0.000345, 0.002567, -0.004089, 0.000912, -0.003456], + sigma: 0.5, + epsilon: 1.0, + }, + { + text: "You need to purchase Google Play gift cards worth $4,789 and read me the codes.", + score: 0.85, + features: { urgency: 0.8, financial_terms: 0.9, gift_card_mention: 1.0, unusual_payment: 1.0 } as Record, + gradient: [0.004567, -0.001234, 0.002890, -0.003456, 0.000789, -0.004123, 0.001567, 0.003012, -0.002345, 0.000678, -0.001890, 0.004234], + sigma: 0.5, + epsilon: 1.0, + }, + { + text: "This is the only way to avoid criminal prosecution. Do it now.", + score: 0.92, + features: { urgency: 1.0, threat_language: 0.95, time_pressure: 1.0, coercion: 0.9 } as Record, + gradient: [-0.002345, 0.004567, -0.000891, 0.003210, -0.001678, 0.002456, -0.004012, 0.000345, 0.001789, -0.003567, 0.002123, -0.000456], + sigma: 0.5, + epsilon: 1.0, + }, +] + +interface PrivacyDemoProps { + sentences: Array<{ + text: string + score: number + features: Record + }> + gradientVectors: number[][] + isCallActive: boolean +} + +export default function PrivacyDemo({ sentences, gradientVectors, isCallActive }: PrivacyDemoProps) { + const [wsConnected, setWsConnected] = useState(false) + const [wsEntries, setWsEntries] = useState([]) + const [fallbackEntries, setFallbackEntries] = useState([]) + const [isReplaying, setIsReplaying] = useState(false) + + const wsRef = useRef(null) + const replayTimerRef = useRef | null>(null) + const isMountedRef = useRef(true) + + // Try connecting to the privacy-demo WebSocket + useEffect(() => { + isMountedRef.current = true + let ws: WebSocket | null = null + + try { + const wsBase = import.meta.env.DEV + ? 'ws://localhost:8000' + : `${window.location.protocol === 'https:' ? 'wss:' : 'ws:'}//${window.location.host}` + ws = new WebSocket(`${wsBase}/ws/privacy-demo`) + wsRef.current = ws + + ws.onopen = () => { + if (isMountedRef.current) { + setWsConnected(true) + } + } + + ws.onmessage = (event) => { + if (!isMountedRef.current) return + try { + const data = JSON.parse(event.data) + const entry: DisplayEntry = { + text: data.text ?? data.transcript ?? '', + score: data.fraud_score ?? data.score ?? 0, + features: data.features ?? {}, + gradient: data.gradient ?? data.noised_gradient ?? [], + sigma: data.sigma ?? 0.5, + epsilon: data.epsilon ?? 1.0, + } + setWsEntries(prev => [...prev, entry]) + } catch { + // Ignore malformed messages + } + } + + ws.onerror = () => { + if (isMountedRef.current) { + setWsConnected(false) + } + } + + ws.onclose = () => { + if (isMountedRef.current) { + setWsConnected(false) + wsRef.current = null + } + } + } catch { + setWsConnected(false) + } + + return () => { + isMountedRef.current = false + if (ws) { + ws.close(1000, 'Component unmount') + } + wsRef.current = null + } + }, []) + + // Determine which data to display + // Priority: WebSocket data > live call data passed via props > fallback examples + const useWebSocketData = wsConnected && wsEntries.length > 0 + const usePropData = !useWebSocketData && sentences.length > 0 + + const displayEntries: DisplayEntry[] = useWebSocketData + ? wsEntries + : usePropData + ? sentences.map((s, i) => ({ + text: s.text, + score: s.score, + features: s.features, + gradient: gradientVectors[i] ?? Array.from({ length: 12 }, () => Number(((Math.random() - 0.5) * 0.01).toFixed(6))), + sigma: 0.5, + epsilon: 1.0, + })) + : fallbackEntries + + // Replay: cycle through fallback examples one by one + const startReplay = useCallback(() => { + if (isReplaying) return + setIsReplaying(true) + setFallbackEntries([]) + setWsEntries([]) + + let index = 0 + const playNext = () => { + if (!isMountedRef.current || index >= FALLBACK_EXAMPLES.length) { + if (isMountedRef.current) setIsReplaying(false) + return + } + const example = FALLBACK_EXAMPLES[index] + setFallbackEntries(prev => [...prev, example]) + index++ + replayTimerRef.current = setTimeout(playNext, 1500) + } + + replayTimerRef.current = setTimeout(playNext, 500) + }, [isReplaying]) + + // Cleanup replay timer + useEffect(() => { + return () => { + if (replayTimerRef.current) clearTimeout(replayTimerRef.current) + } + }, []) + + return ( +
+ {/* Intro banner */} +
+
+
+ +
+
+

Privacy-First Architecture

+

+ SentinelEdge processes all sensitive data on your device. The hub server only receives + differentially-private gradient updates -- mathematical noise that cannot be reverse-engineered + to reconstruct your conversations or personal information. +

+
+
+ {/* Connection status */} +
+ {wsConnected ? ( + <> + + Live + + ) : ( + <> + + Offline + + )} +
+ {/* Replay button */} + +
+
+
+ + {/* Side by side panels with noise wall */} +
+ {/* Device panel */} +
+ {/* Header */} +
+
+ +
+
+

Your Device

+

Everything stays here

+
+ +
+ + {/* Content */} +
+ {displayEntries.length === 0 && !isCallActive ? ( +
+ +

Start a call to see device-side data

+

Full transcript and analysis visible here

+

+ Or press Replay Demo above +

+
+ ) : ( + displayEntries.map((entry, i) => ( +
+ {/* Transcript text */} +
+

Transcript #{i + 1}

+

+ {entry.text} +

+
+ + {/* Features + score */} +
+
+

Fraud Score

+

+ {(entry.score * 100).toFixed(1)}% +

+
+
+

Features

+
+ {Object.entries(entry.features) + .filter(([, v]) => v > 0.3) + .slice(0, 3) + .map(([k]) => ( + + {k.replace(/_/g, ' ')} + + )) + } +
+
+
+ + {i < displayEntries.length - 1 && ( +
+ )} +
+ )) + )} +
+
+ + {/* Visual noise wall - dashed vertical privacy boundary */} +
+ {/* Top label */} +
+

Privacy

+
+ + {/* Dashed line */} +
+
+ {/* Noise glow effect */} +
+
+ + {/* Lock icon in the middle */} +
+ +
+ + {/* Bottom label */} +
+

Barrier

+
+
+ + {/* Hub server panel */} +
+ {/* Header */} +
+
+ +
+
+

Hub Server

+

This is all we send

+
+ +
+ + {/* Content */} +
+ {displayEntries.length === 0 && !isCallActive ? ( +
+ +

No data transmitted yet

+

Only noisy gradients are sent

+
+ ) : ( + displayEntries.map((entry, i) => ( +
+
+
+

+ Gradient Update #{i + 1} +

+ + DP-SGD + +
+
+ [{entry.gradient.map((v, j) => ( + + 0.005 ? 'text-gray-400' : 'text-gray-700' + }> + {v.toFixed(6)} + + {j < entry.gradient.length - 1 ? ', ' : ''} + + ))}] +
+
+
+ +

+ sigma={entry.sigma.toFixed(1)} +

+
+
+ +

+ epsilon={entry.epsilon.toFixed(1)} +

+
+
+
+
+ )) + )} +
+
+
+ + {/* Bottom explanation */} +
+
+
+
+ +
+ +
+ +
+ +
+ +
+
+
+
+ + Zero-Knowledge Fraud Detection +
+

+ Raw audio and transcripts never leave your device. Only differentially-private model gradients + are transmitted. Even if the hub server is compromised, your conversation data remains private. + Mathematical guarantees ensure no individual call can be reconstructed from gradient updates. +

+
+
+
+
+ ) +} diff --git a/demo/frontend/src/components/ScoreGauge.tsx b/demo/frontend/src/components/ScoreGauge.tsx new file mode 100644 index 0000000000000000000000000000000000000000..94310f7053a9295a24350b258d0b9982be0003b1 --- /dev/null +++ b/demo/frontend/src/components/ScoreGauge.tsx @@ -0,0 +1,260 @@ +import { useEffect, useMemo, useRef, useState, type CSSProperties } from 'react' +import { ShieldCheck, ShieldAlert, ShieldX } from 'lucide-react' + +interface ScoreGaugeProps { + score: number + label?: string +} + +interface ParticleBurst { + id: number + color: string +} + +const THRESHOLDS = [0.3, 0.5, 0.75] +const GRADIENT_STOPS = [ + { score: 0, color: '#10B981' }, + { score: 0.3, color: '#EAB308' }, + { score: 0.5, color: '#F59E0B' }, + { score: 0.75, color: '#F97316' }, + { score: 1, color: '#EF4444' }, +] + +function hexToRgb(hex: string) { + const normalized = hex.replace('#', '') + const value = Number.parseInt(normalized, 16) + return { + r: (value >> 16) & 255, + g: (value >> 8) & 255, + b: value & 255, + } +} + +function interpolateColor(score: number): string { + const safeScore = Math.max(0, Math.min(1, score)) + + for (let index = 1; index < GRADIENT_STOPS.length; index += 1) { + const start = GRADIENT_STOPS[index - 1] + const end = GRADIENT_STOPS[index] + + if (safeScore <= end.score) { + const range = end.score - start.score || 1 + const progress = (safeScore - start.score) / range + const startRgb = hexToRgb(start.color) + const endRgb = hexToRgb(end.color) + + const r = Math.round(startRgb.r + (endRgb.r - startRgb.r) * progress) + const g = Math.round(startRgb.g + (endRgb.g - startRgb.g) * progress) + const b = Math.round(startRgb.b + (endRgb.b - startRgb.b) * progress) + + return `rgb(${r}, ${g}, ${b})` + } + } + + return GRADIENT_STOPS[GRADIENT_STOPS.length - 1].color +} + +function getLevelText(score: number): string { + if (score < 0.15) return 'Safe' + if (score < 0.3) return 'Low Risk' + if (score < 0.5) return 'Moderate' + if (score < 0.75) return 'High Risk' + return 'Critical' +} + +function getLevelIcon(score: number) { + if (score < 0.3) return + if (score < 0.75) return + return +} + +export default function ScoreGauge({ score, label = 'Fraud Score' }: ScoreGaugeProps) { + const clampedScore = Math.max(0, Math.min(1, score)) + const previousScoreRef = useRef(clampedScore) + const burstIdRef = useRef(0) + const [bursts, setBursts] = useState([]) + const [isThresholdPulseActive, setIsThresholdPulseActive] = useState(false) + + const percentage = Math.round(clampedScore * 100) + const color = useMemo(() => interpolateColor(clampedScore), [clampedScore]) + + const size = 200 + const strokeWidth = 12 + const radius = (size - strokeWidth) / 2 + const circumference = Math.PI * radius + const offset = circumference - clampedScore * circumference + const centerX = size / 2 + const centerY = size / 2 + + useEffect(() => { + const previousScore = previousScoreRef.current + const crossedThreshold = THRESHOLDS.some( + threshold => + (previousScore < threshold && clampedScore >= threshold) || + (previousScore > threshold && clampedScore <= threshold) + ) + + previousScoreRef.current = clampedScore + + if (!crossedThreshold) return + + const burst = { + id: burstIdRef.current, + color, + } + burstIdRef.current += 1 + + setBursts(current => [...current, burst]) + setIsThresholdPulseActive(true) + + const pulseTimeout = window.setTimeout(() => { + setIsThresholdPulseActive(false) + }, 700) + + const cleanupTimeout = window.setTimeout(() => { + setBursts(current => current.filter(item => item.id !== burst.id)) + }, 1300) + + return () => { + window.clearTimeout(pulseTimeout) + window.clearTimeout(cleanupTimeout) + } + }, [clampedScore, color]) + + return ( +
+
+
+ + {bursts.map(burst => ( + + ))} + + + + + + + + + + + + + + {[0, 0.25, 0.5, 0.75, 1].map(tick => { + const angle = (tick * 180 - 90) * (Math.PI / 180) + const outerR = radius + strokeWidth / 2 + 4 + const innerR = radius + strokeWidth / 2 + 10 + const x1 = centerX + outerR * Math.cos(angle) + const y1 = centerY + outerR * Math.sin(angle) + const x2 = centerX + innerR * Math.cos(angle) + const y2 = centerY + innerR * Math.sin(angle) + return ( + + ) + })} + + +
+
+ {getLevelIcon(clampedScore)} +
+ + {percentage} + % + + + {getLevelText(clampedScore)} + +
+
+ +

{label}

+ +
+ {[ + { label: 'Safe', color: '#10B981' }, + { label: 'Warning', color: '#EAB308' }, + { label: 'High', color: '#F97316' }, + { label: 'Critical', color: '#EF4444' }, + ].map(item => ( +
+
+ {item.label} +
+ ))} +
+
+ ) +} diff --git a/demo/frontend/src/components/TranscriptPanel.tsx b/demo/frontend/src/components/TranscriptPanel.tsx new file mode 100644 index 0000000000000000000000000000000000000000..e83ce8c170c32d778fbba21bb57d51b484995375 --- /dev/null +++ b/demo/frontend/src/components/TranscriptPanel.tsx @@ -0,0 +1,212 @@ +import { useEffect, useMemo, useRef, useState } from 'react' +import { MessageSquare, Radio } from 'lucide-react' + +interface TranscriptPanelProps { + sentences: Array<{ + text: string + score: number + index: number + }> + isStreaming: boolean +} + +const KEYWORD_STYLES: Array<{ pattern: RegExp; className: string }> = [ + { pattern: /\b(immediately|urgent|hurry|deadline|today|now)\b/gi, className: 'transcript-keyword transcript-keyword-urgent' }, + { pattern: /\b(gift cards?|google play|codes?)\b/gi, className: 'transcript-keyword transcript-keyword-payment' }, + { pattern: /\b(password|pin|social security|verification code|bank details?|ssn)\b/gi, className: 'transcript-keyword transcript-keyword-credential' }, + { pattern: /\b(arrest|warrant|prosecution|criminal)\b/gi, className: 'transcript-keyword transcript-keyword-threat' }, + { pattern: /\b(remote access|download|website|click|login)\b/gi, className: 'transcript-keyword transcript-keyword-action' }, +] + +function getScoreColor(score: number): string { + if (score < 0.3) return 'border-safe' + if (score < 0.5) return 'border-warning' + if (score < 0.75) return 'border-orange-500' + return 'border-alert' +} + +function getScoreBg(score: number): string { + if (score < 0.3) return 'bg-safe/5' + if (score < 0.5) return 'bg-warning/5' + if (score < 0.75) return 'bg-orange-500/5' + return 'bg-alert/5' +} + +function getScoreBadge(score: number): { bg: string; text: string; pulse: boolean } { + if (score < 0.3) return { bg: 'bg-safe/10 text-safe', text: 'Safe', pulse: false } + if (score < 0.5) return { bg: 'bg-warning/10 text-warning', text: 'Caution', pulse: false } + if (score < 0.75) return { bg: 'bg-orange-500/10 text-orange-400', text: 'Suspicious', pulse: true } + return { bg: 'bg-alert/10 text-alert', text: 'Danger', pulse: true } +} + +function highlightKeywords(text: string) { + if (!text) return null + + const matches: Array<{ start: number; end: number; className: string }> = [] + + KEYWORD_STYLES.forEach(({ pattern, className }) => { + const regex = new RegExp(pattern.source, pattern.flags) + let match = regex.exec(text) + while (match) { + matches.push({ + start: match.index, + end: match.index + match[0].length, + className, + }) + match = regex.exec(text) + } + }) + + matches.sort((a, b) => a.start - b.start || b.end - a.end) + + const segments: Array<{ text: string; className?: string }> = [] + let cursor = 0 + + matches.forEach(match => { + if (match.start < cursor) return + if (match.start > cursor) { + segments.push({ text: text.slice(cursor, match.start) }) + } + segments.push({ + text: text.slice(match.start, match.end), + className: match.className, + }) + cursor = match.end + }) + + if (cursor < text.length) { + segments.push({ text: text.slice(cursor) }) + } + + return segments.map((segment, index) => ( + + {segment.text} + + )) +} + +interface TypewriterSentenceProps { + text: string + isLatest: boolean +} + +function TypewriterSentence({ text, isLatest }: TypewriterSentenceProps) { + const [visibleLength, setVisibleLength] = useState(isLatest ? 0 : text.length) + + useEffect(() => { + if (!isLatest) { + setVisibleLength(text.length) + return + } + + setVisibleLength(0) + const stepMs = Math.max(16, Math.min(34, 520 / Math.max(text.length, 1))) + const interval = window.setInterval(() => { + setVisibleLength(current => { + if (current >= text.length) { + window.clearInterval(interval) + return current + } + return current + 1 + }) + }, stepMs) + + return () => window.clearInterval(interval) + }, [isLatest, text]) + + const visibleText = useMemo(() => text.slice(0, visibleLength), [text, visibleLength]) + const isTyping = isLatest && visibleLength < text.length + + return ( + + {highlightKeywords(visibleText)} + + ) +} + +export default function TranscriptPanel({ sentences, isStreaming }: TranscriptPanelProps) { + const scrollRef = useRef(null) + + useEffect(() => { + if (scrollRef.current) { + scrollRef.current.scrollTop = scrollRef.current.scrollHeight + } + }, [sentences]) + + return ( +
+
+
+ +

Live Transcript

+
+ {isStreaming && ( +
+ + Streaming +
+ )} +
+ +
+ {sentences.length === 0 ? ( +
+
+ +

Waiting for transcript...

+

Start a sample call to see real-time analysis

+
+
+ ) : ( + sentences.map((sentence, i) => { + const badge = getScoreBadge(sentence.score) + const isLatest = i === sentences.length - 1 + + return ( +
+ + {sentence.index + 1} + + +

+ +

+ +
+ + {badge.text} + + + {(sentence.score * 100).toFixed(0)}% + +
+
+ ) + }) + )} + + {isStreaming && sentences.length > 0 && ( +
+
+
+
+
+
+ Listening... +
+ )} +
+
+ ) +} diff --git a/demo/frontend/src/hooks/useWebSocket.ts b/demo/frontend/src/hooks/useWebSocket.ts new file mode 100644 index 0000000000000000000000000000000000000000..317cbc6b20e150266f973ef3f3daa73ae1971196 --- /dev/null +++ b/demo/frontend/src/hooks/useWebSocket.ts @@ -0,0 +1,112 @@ +import { useRef, useState, useCallback, useEffect } from 'react' + +interface UseWebSocketOptions { + url: string + onMessage: (data: any) => void + onOpen?: () => void + onClose?: () => void + autoConnect?: boolean +} + +interface UseWebSocketReturn { + connect: () => void + disconnect: () => void + send: (data: any) => void + isConnected: boolean + error: string | null +} + +export function useWebSocket(options: UseWebSocketOptions): UseWebSocketReturn { + const { url, onMessage, onOpen, onClose, autoConnect = false } = options + const [isConnected, setIsConnected] = useState(false) + const [error, setError] = useState(null) + const wsRef = useRef(null) + const reconnectTimeoutRef = useRef | null>(null) + const onMessageRef = useRef(onMessage) + const onOpenRef = useRef(onOpen) + const onCloseRef = useRef(onClose) + + // Keep callback refs fresh without causing reconnections + useEffect(() => { + onMessageRef.current = onMessage + onOpenRef.current = onOpen + onCloseRef.current = onClose + }, [onMessage, onOpen, onClose]) + + const disconnect = useCallback(() => { + if (reconnectTimeoutRef.current) { + clearTimeout(reconnectTimeoutRef.current) + reconnectTimeoutRef.current = null + } + if (wsRef.current) { + wsRef.current.close(1000, 'Client disconnect') + wsRef.current = null + } + setIsConnected(false) + }, []) + + const connect = useCallback(() => { + if (!url) return + disconnect() + setError(null) + + try { + const ws = new WebSocket(url) + wsRef.current = ws + + ws.onopen = () => { + setIsConnected(true) + setError(null) + onOpenRef.current?.() + } + + ws.onmessage = (event) => { + try { + const data = JSON.parse(event.data) + onMessageRef.current(data) + } catch { + // If not JSON, pass raw data + onMessageRef.current(event.data) + } + } + + ws.onerror = () => { + setError('WebSocket connection error') + } + + ws.onclose = (event) => { + setIsConnected(false) + wsRef.current = null + onCloseRef.current?.() + + // Auto-reconnect on abnormal closure (not manual disconnect) + if (event.code !== 1000 && event.code !== 1005) { + reconnectTimeoutRef.current = setTimeout(() => { + connect() + }, 3000) + } + } + } catch (err) { + setError(`Failed to connect: ${err}`) + setIsConnected(false) + } + }, [url, disconnect]) + + const send = useCallback((data: any) => { + if (wsRef.current?.readyState === WebSocket.OPEN) { + wsRef.current.send(typeof data === 'string' ? data : JSON.stringify(data)) + } + }, []) + + // Auto-connect on mount if configured + useEffect(() => { + if (autoConnect && url) { + connect() + } + return () => { + disconnect() + } + }, [autoConnect, url, connect, disconnect]) + + return { connect, disconnect, send, isConnected, error } +} diff --git a/demo/frontend/src/main.tsx b/demo/frontend/src/main.tsx new file mode 100644 index 0000000000000000000000000000000000000000..9e245e774f1c050d4a889d8fe7bf2c2f30e72641 --- /dev/null +++ b/demo/frontend/src/main.tsx @@ -0,0 +1,11 @@ +import React from 'react' +import ReactDOM from 'react-dom/client' +import App from './App' +import './styles/index.css' +import './styles/phone.css' + +ReactDOM.createRoot(document.getElementById('root')!).render( + + + , +) diff --git a/demo/frontend/src/styles/index.css b/demo/frontend/src/styles/index.css new file mode 100644 index 0000000000000000000000000000000000000000..705967cc52dbf021d908321ff714f69f6ac54004 --- /dev/null +++ b/demo/frontend/src/styles/index.css @@ -0,0 +1,120 @@ +@tailwind base; +@tailwind components; +@tailwind utilities; + +@layer base { + * { + box-sizing: border-box; + } + + body { + @apply bg-dark-bg text-gray-100 font-sans; + -webkit-font-smoothing: antialiased; + -moz-osx-font-smoothing: grayscale; + } + + button { + transition: + transform 180ms ease, + background-color 180ms ease, + border-color 180ms ease, + color 180ms ease, + box-shadow 180ms ease, + opacity 180ms ease; + } + + button:hover:not(:disabled) { + transform: translateY(-1px); + } + + button:active:not(:disabled) { + transform: translateY(0) scale(0.98); + } + + button:disabled { + cursor: not-allowed; + } + + button:focus-visible { + outline: 2px solid rgba(20, 184, 166, 0.5); + outline-offset: 2px; + } + + ::-webkit-scrollbar { + width: 6px; + height: 6px; + } + + ::-webkit-scrollbar-track { + @apply bg-dark-bg; + } + + ::-webkit-scrollbar-thumb { + @apply bg-dark-surface rounded-full; + } + + ::-webkit-scrollbar-thumb:hover { + @apply bg-dark-border; + } +} + +@layer components { + .glass-card { + @apply bg-dark-card/80 backdrop-blur-xl border border-dark-border/50 rounded-2xl; + } + + .control-button { + @apply inline-flex items-center justify-center gap-2 rounded-lg border border-dark-border/50 bg-dark-card text-gray-300 shadow-sm shadow-black/10; + } + + .control-button:hover:not(:disabled) { + @apply border-brand-teal/30 bg-brand-teal/10 text-brand-teal; + box-shadow: 0 10px 25px rgba(13, 148, 136, 0.08); + } + + .control-button:disabled { + @apply bg-dark-card/70 text-gray-600 border-dark-border/40 opacity-50; + } + + .skeleton-block { + position: relative; + overflow: hidden; + border-radius: 0.75rem; + background: linear-gradient(90deg, rgba(51, 65, 85, 0.55) 0%, rgba(71, 85, 105, 0.75) 50%, rgba(51, 65, 85, 0.55) 100%); + background-size: 200% 100%; + animation: skeleton-wave 1.6s ease-in-out infinite; + } + + .panel-skeleton { + @apply space-y-3; + } + + .panel-skeleton-card { + @apply rounded-2xl border border-dark-border/30 bg-dark-bg/30 p-4; + } + + @keyframes skeleton-wave { + 0% { + background-position: 200% 0; + } + 100% { + background-position: -200% 0; + } + } + + .glow-teal { + box-shadow: 0 0 20px rgba(13, 148, 136, 0.15), 0 0 60px rgba(13, 148, 136, 0.05); + } + + .glow-red { + box-shadow: 0 0 20px rgba(239, 68, 68, 0.2), 0 0 60px rgba(239, 68, 68, 0.1); + } + + .tab-active { + @apply text-brand-teal border-b-2 border-brand-teal; + } + + .tab-inactive { + @apply text-gray-400 hover:text-gray-200 border-b-2 border-transparent; + } +} diff --git a/demo/frontend/src/styles/phone.css b/demo/frontend/src/styles/phone.css new file mode 100644 index 0000000000000000000000000000000000000000..92fd2cf338c498c75d658ad60c3a5a1f731d1700 --- /dev/null +++ b/demo/frontend/src/styles/phone.css @@ -0,0 +1,535 @@ +/* ===== Phone Frame Styling ===== */ + +.phone-frame { + perspective: 1000px; + filter: drop-shadow(0 25px 50px rgba(0, 0, 0, 0.5)) + drop-shadow(0 12px 24px rgba(0, 0, 0, 0.3)); +} + +.phone-frame:hover { + transform: translateY(-2px); + transition: transform 0.3s ease; +} + +.phone-screen-shake { + animation: phone-screen-shake 480ms cubic-bezier(0.36, 0.07, 0.19, 0.97); +} + +@keyframes phone-screen-shake { + 0%, + 100% { + transform: translate3d(0, 0, 0); + } + 18% { + transform: translate3d(-1.5px, 0, 0); + } + 36% { + transform: translate3d(1.25px, 0, 0); + } + 54% { + transform: translate3d(-1px, 0.4px, 0); + } + 72% { + transform: translate3d(0.8px, -0.4px, 0); + } +} + +/* ===== Pulse Animation for Critical Alerts ===== */ + +@keyframes pulse-border { + 0%, + 100% { + box-shadow: 0 0 0 0 rgba(239, 68, 68, 0.6), + inset 0 0 20px rgba(239, 68, 68, 0.1); + } + 50% { + box-shadow: 0 0 20px 5px rgba(239, 68, 68, 0.3), + inset 0 0 30px rgba(239, 68, 68, 0.15); + } +} + +.pulse-border { + animation: pulse-border 1.5s ease-in-out infinite; +} + +/* ===== Typewriter Effect for Transcript ===== */ + +@keyframes typewriter-cursor { + 0%, + 100% { + opacity: 1; + } + 50% { + opacity: 0; + } +} + +.typewriter-cursor::after { + content: "|"; + animation: typewriter-cursor 0.8s ease-in-out infinite; + color: #0d9488; + font-weight: bold; + margin-left: 1px; +} + +.transcript-keyword { + font-weight: 700; + border-radius: 0.35rem; + padding: 0 0.12rem; +} + +.transcript-keyword-urgent { + color: #fbbf24; + background: rgba(245, 158, 11, 0.12); +} + +.transcript-keyword-payment { + color: #fb923c; + background: rgba(249, 115, 22, 0.12); +} + +.transcript-keyword-credential { + color: #f87171; + background: rgba(239, 68, 68, 0.12); +} + +.transcript-keyword-threat { + color: #ef4444; + background: rgba(127, 29, 29, 0.24); +} + +.transcript-keyword-action { + color: #2dd4bf; + background: rgba(13, 148, 136, 0.12); +} + +.transcript-score-badge-pulse { + animation: transcript-badge-pulse 1.25s ease-in-out infinite; + box-shadow: 0 0 0 rgba(249, 115, 22, 0); +} + +@keyframes transcript-badge-pulse { + 0%, + 100% { + transform: scale(1); + box-shadow: 0 0 0 0 rgba(249, 115, 22, 0); + } + 50% { + transform: scale(1.06); + box-shadow: 0 0 18px 0 rgba(249, 115, 22, 0.18); + } +} + +/* ===== Slide Down for Alert Overlay ===== */ + +.alert-slide-enter { + animation: alert-slide 0.4s cubic-bezier(0.32, 0.72, 0, 1) forwards; +} + +.alert-overlay { + animation: alert-overlay-fade 420ms ease-out both; +} + +.alert-overlay-critical { + animation: alert-overlay-fade 420ms ease-out both; +} + +.alert-overlay-panel { + transform-origin: top center; + animation: alert-overlay-spring 620ms cubic-bezier(0.18, 0.9, 0.22, 1.16) both; +} + +.alert-banner { + animation: alert-banner-spring 560ms cubic-bezier(0.22, 0.88, 0.28, 1.14) both; +} + +.alert-banner-card { + transform-origin: top center; +} + +@keyframes alert-slide { + 0% { + transform: translateY(-100%); + opacity: 0; + } + 60% { + opacity: 1; + } + 100% { + transform: translateY(0); + opacity: 1; + } +} + +@keyframes alert-overlay-fade { + 0% { + opacity: 0; + } + 100% { + opacity: 1; + } +} + +@keyframes alert-overlay-spring { + 0% { + transform: translateY(-30px) scale(0.97); + opacity: 0; + } + 55% { + transform: translateY(8px) scale(1.01); + opacity: 1; + } + 78% { + transform: translateY(-3px) scale(0.998); + } + 100% { + transform: translateY(0) scale(1); + opacity: 1; + } +} + +@keyframes alert-banner-spring { + 0% { + transform: translateY(-22px) scale(0.96); + opacity: 0; + } + 60% { + transform: translateY(4px) scale(1.01); + opacity: 1; + } + 82% { + transform: translateY(-2px) scale(0.998); + } + 100% { + transform: translateY(0) scale(1); + opacity: 1; + } +} + +/* ===== Smooth Gauge Transitions ===== */ + +.gauge-arc { + transition: stroke-dashoffset 0.8s cubic-bezier(0.22, 1, 0.36, 1), + stroke 0.45s ease, + filter 0.45s ease; + filter: drop-shadow(0 0 10px color-mix(in srgb, var(--gauge-color) 40%, transparent)); +} + +.gauge-needle { + transition: transform 0.6s cubic-bezier(0.4, 0, 0.2, 1); + transform-origin: center center; +} + +.score-gauge-shell { + isolation: isolate; +} + +.score-gauge-svg { + filter: drop-shadow(0 0 12px color-mix(in srgb, var(--gauge-color) 28%, transparent)); + transition: filter 0.45s ease; +} + +.score-gauge-glow { + background: + radial-gradient(circle at center, color-mix(in srgb, var(--gauge-color) 22%, transparent) 0%, transparent 68%); + filter: blur(8px); + opacity: 0.95; + transition: background 0.45s ease, opacity 0.45s ease, transform 0.7s ease; +} + +.score-gauge-center { + transition: color 0.45s ease, transform 0.35s ease, filter 0.45s ease; +} + +.score-gauge-threshold-pulse .score-gauge-glow { + animation: score-gauge-threshold-glow 700ms cubic-bezier(0.22, 1, 0.36, 1); +} + +.score-gauge-threshold-pulse .score-gauge-center { + animation: score-gauge-center-pop 520ms cubic-bezier(0.22, 1, 0.36, 1); +} + +.score-gauge-burst { + overflow: visible; +} + +.score-gauge-particle { + width: 6px; + height: 6px; + margin-left: -3px; + margin-top: -3px; + border-radius: 9999px; + background: + radial-gradient(circle at 30% 30%, rgba(255, 255, 255, 0.95), var(--burst-color) 58%, transparent 72%); + box-shadow: 0 0 14px color-mix(in srgb, var(--burst-color) 60%, transparent); + opacity: 0; + transform: rotate(var(--particle-angle)) translateY(0) scale(0.4); + animation: score-gauge-particle-burst 860ms cubic-bezier(0.2, 0.9, 0.3, 1) forwards; +} + +@keyframes score-gauge-threshold-glow { + 0% { + transform: scale(0.92); + opacity: 0.55; + } + 55% { + transform: scale(1.08); + opacity: 1; + } + 100% { + transform: scale(1); + opacity: 0.95; + } +} + +@keyframes score-gauge-center-pop { + 0% { + transform: scale(0.96); + } + 55% { + transform: scale(1.05); + } + 100% { + transform: scale(1); + } +} + +@keyframes score-gauge-particle-burst { + 0% { + opacity: 0; + transform: rotate(var(--particle-angle)) translateY(0) scale(0.4); + } + 20% { + opacity: 1; + } + 100% { + opacity: 0; + transform: rotate(var(--particle-angle)) translateY(calc(-1 * var(--particle-distance))) scale(0.95); + } +} + +/* ===== Score Bar Animations ===== */ + +.score-bar { + transition: width 0.7s cubic-bezier(0.4, 0, 0.2, 1), + background-color 0.3s ease; +} + +/* ===== Gradient Vector Scroll ===== */ + +.gradient-scroll { + mask-image: linear-gradient( + to bottom, + transparent, + black 5%, + black 95%, + transparent + ); + -webkit-mask-image: linear-gradient( + to bottom, + transparent, + black 5%, + black 95%, + transparent + ); +} + +/* ===== Glass Effect Enhancement ===== */ + +.glass-effect { + background: rgba(30, 41, 59, 0.6); + backdrop-filter: blur(20px) saturate(150%); + -webkit-backdrop-filter: blur(20px) saturate(150%); +} + +/* ===== Streaming Dots ===== */ + +@keyframes streaming-dot { + 0%, + 80%, + 100% { + transform: scale(0.6); + opacity: 0.4; + } + 40% { + transform: scale(1); + opacity: 1; + } +} + +.streaming-dot:nth-child(1) { + animation: streaming-dot 1.4s ease-in-out 0s infinite; +} +.streaming-dot:nth-child(2) { + animation: streaming-dot 1.4s ease-in-out 0.2s infinite; +} +.streaming-dot:nth-child(3) { + animation: streaming-dot 1.4s ease-in-out 0.4s infinite; +} + +/* ===== Tab Underline Animation ===== */ + +.tab-underline { + position: relative; +} + +.tab-underline::after { + content: ""; + position: absolute; + bottom: -2px; + left: 0; + right: 0; + height: 2px; + background: #0d9488; + transform: scaleX(0); + transition: transform 0.2s ease; +} + +.tab-underline.active::after { + transform: scaleX(1); +} + +/* ===== Feature Bar Hover ===== */ + +.feature-bar-container:hover .feature-bar { + filter: brightness(1.2); +} + +.feature-bar-container { + animation: feature-bar-slide-in 520ms cubic-bezier(0.22, 1, 0.36, 1) both; +} + +.feature-track { + background: + linear-gradient(90deg, var(--feature-track), rgba(15, 23, 42, 0.26)); + transition: background 0.35s ease; +} + +.feature-bar { + background: + linear-gradient(90deg, color-mix(in srgb, var(--feature-color) 80%, white 8%) 0%, var(--feature-color) 55%, color-mix(in srgb, var(--feature-color) 75%, black 6%) 100%); + box-shadow: 0 0 18px var(--feature-glow); + transform-origin: left center; + animation: feature-bar-fill 700ms cubic-bezier(0.22, 1, 0.36, 1) both; + transition: width 0.75s cubic-bezier(0.22, 1, 0.36, 1), background 0.4s ease, box-shadow 0.4s ease, filter 0.25s ease; +} + +.feature-tooltip { + opacity: 0; + transform: translate(-50%, 8px) scale(0.98); + transition: opacity 0.2s ease, transform 0.2s ease; +} + +.feature-tooltip::before { + content: ""; + position: absolute; + top: -6px; + left: 50%; + width: 12px; + height: 12px; + border-left: 1px solid rgba(255, 255, 255, 0.1); + border-top: 1px solid rgba(255, 255, 255, 0.1); + background: rgba(2, 6, 23, 0.95); + transform: translateX(-50%) rotate(45deg); +} + +.feature-tooltip-trigger:hover .feature-tooltip { + opacity: 1; + transform: translate(-50%, 0) scale(1); +} + +@keyframes feature-bar-slide-in { + 0% { + opacity: 0; + transform: translateX(-18px); + } + 100% { + opacity: 1; + transform: translateX(0); + } +} + +@keyframes feature-bar-fill { + 0% { + transform: scaleX(0); + opacity: 0.6; + } + 100% { + transform: scaleX(1); + opacity: 1; + } +} + +/* ===== Ring Pulse for Avatar ===== */ + +@keyframes ring-pulse { + 0% { + transform: scale(1); + opacity: 0.4; + } + 100% { + transform: scale(1.5); + opacity: 0; + } +} + +.ring-pulse { + animation: ring-pulse 2s ease-out infinite; +} + +/* ===== Privacy Panel Arrow Animation ===== */ + +@keyframes flow-arrow { + 0% { + transform: translateX(-4px); + opacity: 0.3; + } + 50% { + opacity: 1; + } + 100% { + transform: translateX(4px); + opacity: 0.3; + } +} + +.flow-arrow { + animation: flow-arrow 1.5s ease-in-out infinite; +} + +/* ===== Smooth Number Transition ===== */ + +.number-transition { + transition: all 0.3s cubic-bezier(0.4, 0, 0.2, 1); +} + +/* ===== Chart Tooltip Styling ===== */ + +.recharts-default-tooltip { + background-color: #1e293b !important; + border: 1px solid #334155 !important; + border-radius: 8px !important; +} + +.recharts-tooltip-label { + color: #94a3b8 !important; +} + +/* ===== Scrollbar for Transcript ===== */ + +.transcript-scroll::-webkit-scrollbar { + width: 4px; +} + +.transcript-scroll::-webkit-scrollbar-track { + background: transparent; +} + +.transcript-scroll::-webkit-scrollbar-thumb { + background: #334155; + border-radius: 2px; +} + +.transcript-scroll::-webkit-scrollbar-thumb:hover { + background: #475569; +} diff --git a/demo/frontend/tailwind.config.js b/demo/frontend/tailwind.config.js new file mode 100644 index 0000000000000000000000000000000000000000..0ebfa31e7e5d67b5b031b8b495d0a5d5b45bc361 --- /dev/null +++ b/demo/frontend/tailwind.config.js @@ -0,0 +1,70 @@ +/** @type {import('tailwindcss').Config} */ +export default { + content: [ + "./index.html", + "./src/**/*.{js,ts,jsx,tsx}", + ], + theme: { + extend: { + colors: { + brand: { + teal: '#0D9488', + 'teal-light': '#14B8A6', + 'teal-dark': '#0F766E', + }, + dark: { + bg: '#0F172A', + card: '#1E293B', + surface: '#334155', + border: '#475569', + }, + safe: { + DEFAULT: '#10B981', + light: '#34D399', + }, + warning: { + DEFAULT: '#F59E0B', + light: '#FBBF24', + }, + alert: { + DEFAULT: '#EF4444', + light: '#F87171', + dark: '#DC2626', + }, + }, + fontFamily: { + mono: ['JetBrains Mono', 'Fira Code', 'monospace'], + sans: ['Inter', 'system-ui', 'sans-serif'], + }, + animation: { + 'pulse-alert': 'pulse-alert 1.5s ease-in-out infinite', + 'slide-down': 'slide-down 0.4s ease-out', + 'fade-in': 'fade-in 0.3s ease-out', + 'gauge-fill': 'gauge-fill 1s ease-out', + 'typewriter': 'typewriter 0.5s ease-out', + }, + keyframes: { + 'pulse-alert': { + '0%, 100%': { opacity: '1', boxShadow: '0 0 0 0 rgba(239, 68, 68, 0.4)' }, + '50%': { opacity: '0.9', boxShadow: '0 0 20px 10px rgba(239, 68, 68, 0.2)' }, + }, + 'slide-down': { + '0%': { transform: 'translateY(-100%)', opacity: '0' }, + '100%': { transform: 'translateY(0)', opacity: '1' }, + }, + 'fade-in': { + '0%': { opacity: '0', transform: 'translateY(8px)' }, + '100%': { opacity: '1', transform: 'translateY(0)' }, + }, + 'gauge-fill': { + '0%': { strokeDashoffset: '283' }, + }, + 'typewriter': { + '0%': { opacity: '0', transform: 'translateX(-4px)' }, + '100%': { opacity: '1', transform: 'translateX(0)' }, + }, + }, + }, + }, + plugins: [], +} diff --git a/demo/frontend/tsconfig.json b/demo/frontend/tsconfig.json new file mode 100644 index 0000000000000000000000000000000000000000..335e6fb21fe91b9907424e3afea365ba80ddaef4 --- /dev/null +++ b/demo/frontend/tsconfig.json @@ -0,0 +1,25 @@ +{ + "compilerOptions": { + "target": "ES2020", + "useDefineForClassFields": true, + "lib": ["ES2020", "DOM", "DOM.Iterable"], + "module": "ESNext", + "skipLibCheck": true, + "moduleResolution": "bundler", + "allowImportingTsExtensions": true, + "resolveJsonModule": true, + "isolatedModules": true, + "noEmit": true, + "jsx": "react-jsx", + "strict": true, + "noUnusedLocals": false, + "noUnusedParameters": false, + "noFallthroughCasesInSwitch": true, + "baseUrl": ".", + "paths": { + "@/*": ["src/*"] + } + }, + "include": ["src"], + "references": [{ "path": "./tsconfig.node.json" }] +} diff --git a/demo/frontend/tsconfig.node.json b/demo/frontend/tsconfig.node.json new file mode 100644 index 0000000000000000000000000000000000000000..42872c59f5b01c9155864572bc2fbd5833a7406c --- /dev/null +++ b/demo/frontend/tsconfig.node.json @@ -0,0 +1,10 @@ +{ + "compilerOptions": { + "composite": true, + "skipLibCheck": true, + "module": "ESNext", + "moduleResolution": "bundler", + "allowSyntheticDefaultImports": true + }, + "include": ["vite.config.ts"] +} diff --git a/demo/frontend/vite.config.ts b/demo/frontend/vite.config.ts new file mode 100644 index 0000000000000000000000000000000000000000..807a4300a95415c8bad8bee45c1641b1ad87e8fa --- /dev/null +++ b/demo/frontend/vite.config.ts @@ -0,0 +1,16 @@ +import { defineConfig } from 'vite' +import react from '@vitejs/plugin-react' + +export default defineConfig({ + plugins: [react()], + server: { + port: 5173, + proxy: { + '/api': 'http://localhost:8000', + '/ws': { + target: 'ws://localhost:8000', + ws: true, + } + } + } +}) diff --git a/deploy_server.py b/deploy_server.py new file mode 100644 index 0000000000000000000000000000000000000000..9b44f23df260767cdfe1a58cb8d7100afa836e15 --- /dev/null +++ b/deploy_server.py @@ -0,0 +1,43 @@ +""" +SentinelEdge deployment server for Hugging Face Spaces. +Adds static file serving to the demo backend app. +""" + +import os +import sys +from pathlib import Path + +sys.path.insert(0, str(Path(__file__).resolve().parent)) + +import uvicorn +from fastapi.staticfiles import StaticFiles +from fastapi.responses import FileResponse + +# The demo backend app already has /api/* and /ws/* routes +from demo.backend.main import app +from hub.server import app as hub_app + +# Mount hub server at /hub +app.mount("/hub", hub_app) + +# Serve built React frontend as static files +static_dir = Path(__file__).parent / "static" + +if static_dir.exists(): + @app.get("/") + async def serve_index() -> FileResponse: + return FileResponse(str(static_dir / "index.html")) + + # Catch-all for SPA client-side routing (must be after API/WS routes) + @app.get("/{path:path}") + async def serve_spa(path: str) -> FileResponse: + file_path = static_dir / path + if file_path.exists() and file_path.is_file(): + return FileResponse(str(file_path)) + return FileResponse(str(static_dir / "index.html")) + + +port = int(os.environ.get("PORT", 7860)) + +if __name__ == "__main__": + uvicorn.run(app, host="0.0.0.0", port=port) diff --git a/federated/__init__.py b/federated/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..28a7c5fcb92118fdec74ed5cf50a2a604894d3fd --- /dev/null +++ b/federated/__init__.py @@ -0,0 +1 @@ +"""Federated learning simulation.""" diff --git a/federated/dp_injector.py b/federated/dp_injector.py new file mode 100644 index 0000000000000000000000000000000000000000..3fc17cd0809daabdb28285face680501fd0792e9 --- /dev/null +++ b/federated/dp_injector.py @@ -0,0 +1,83 @@ +"""Differential privacy noise injection for federated MLP updates.""" +import numpy as np + + +class DPInjector: + """Add calibrated Gaussian noise to MLP gradient deltas before transmission. + + Implements the Gaussian mechanism for (epsilon, delta)-differential privacy: + 1. Clip gradient delta to bounded L2 norm (max_grad_norm) + 2. Add Gaussian noise calibrated to the sensitivity + 3. Track cumulative privacy budget across rounds + """ + + def __init__(self, epsilon: float = 0.3, delta: float = 1e-5, + max_grad_norm: float = 1.0): + self.epsilon = epsilon + self.delta = delta + self.max_grad_norm = max_grad_norm + self.rounds_participated = 0 + + def clip_gradient(self, gradient: np.ndarray) -> np.ndarray: + """Clip gradient delta to max_grad_norm for bounded sensitivity.""" + norm = np.linalg.norm(gradient) + if norm > self.max_grad_norm: + gradient = gradient * (self.max_grad_norm / norm) + return gradient + + def compute_sigma(self, n_local_samples: int) -> float: + """Compute noise standard deviation. + + sensitivity = max_grad_norm / n_local_samples + sigma = sensitivity * sqrt(2 * ln(1.25 / delta)) / epsilon + """ + sensitivity = self.max_grad_norm / max(n_local_samples, 1) + sigma = sensitivity * np.sqrt(2.0 * np.log(1.25 / self.delta)) / self.epsilon + return sigma + + def add_noise(self, gradient_delta: np.ndarray, + n_local_samples: int) -> tuple: + """Apply DP to a gradient delta: clip, noise, return. + + Args: + gradient_delta: Flat 1D array (MLP weight delta). + n_local_samples: Number of local training samples. + + Returns: + (noised_delta, sigma_used, epsilon_spent_this_round) + """ + # Step 1: Clip the gradient delta to bounded L2 norm + clipped = self.clip_gradient(gradient_delta) + + # Step 2: Compute calibrated noise scale + sigma = self.compute_sigma(n_local_samples) + + # Step 3: Add Gaussian noise + noise = np.random.normal(0.0, sigma, size=clipped.shape) + noised_delta = clipped + noise + + # Track rounds + self.rounds_participated += 1 + + return noised_delta, sigma, self.epsilon + + def privacy_budget_spent(self, n_rounds: int = None) -> float: + """Track cumulative privacy budget using advanced composition theorem. + + Total epsilon after k rounds: + eps_total = epsilon * sqrt(2 * k * ln(1/delta)) + k * epsilon * (exp(epsilon) - 1) + + Args: + n_rounds: Number of rounds to compute budget for. + If None, uses self.rounds_participated. + """ + k = n_rounds if n_rounds is not None else self.rounds_participated + if k == 0: + return 0.0 + + eps = self.epsilon + d = self.delta + + term1 = eps * np.sqrt(2.0 * k * np.log(1.0 / d)) + term2 = k * eps * (np.exp(eps) - 1.0) + return term1 + term2 diff --git a/federated/local_trainer.py b/federated/local_trainer.py new file mode 100644 index 0000000000000000000000000000000000000000..9a561d685b0a2235741b6b6538fa782c5b7e923e --- /dev/null +++ b/federated/local_trainer.py @@ -0,0 +1,211 @@ +"""On-device local training with real MLP backpropagation.""" +import numpy as np +from dataclasses import dataclass, field + + +@dataclass +class LocalTrainingBuffer: + """Encrypted local label store. Stores feature vectors + labels only.""" + features: list = field(default_factory=list) + labels: list = field(default_factory=list) + max_samples: int = 500 + + def add(self, feature_vector: np.ndarray, label: int): + """Add a training sample. Auto-evicts oldest if at capacity.""" + if len(self.labels) >= self.max_samples: + self.features.pop(0) + self.labels.pop(0) + self.features.append(feature_vector.copy()) + self.labels.append(label) + + def size(self) -> int: + return len(self.labels) + + def to_arrays(self) -> tuple: + """Return (features_array, labels_array) as numpy arrays.""" + if len(self.features) == 0: + return np.array([]).reshape(0, 0), np.array([], dtype=int) + X = np.vstack(self.features) + y = np.array(self.labels, dtype=int) + return X, y + + +class LocalTrainer: + """On-device model fine-tuning via real mini-batch SGD on a 3-layer MLP. + + The MLP architecture mirrors MLPClassifier: + input -> hidden1 (128, ReLU) -> hidden2 (64, ReLU) -> output (1, Sigmoid) + + Fine-tuning performs real numpy backpropagation through all three layers + with BCE loss. + """ + + # MLP architecture constants (must match MLPClassifier) + HIDDEN1_DIM = 128 + HIDDEN2_DIM = 64 + OUTPUT_DIM = 1 + + def __init__(self, device_id: str, input_dim: int = 402): + self.device_id = device_id + self.input_dim = input_dim + self.buffer = LocalTrainingBuffer() + self.current_model_version = 0 + + def ingest_call_data(self, features: np.ndarray, label: int): + """Store a labeled sample from user feedback.""" + self.buffer.add(features, label) + + def _unpack_weights(self, flat_weights: np.ndarray) -> dict: + """Unpack a flat weight vector into the six MLP parameter arrays.""" + idx = 0 + params = {} + + size = self.input_dim * self.HIDDEN1_DIM + params['W1'] = flat_weights[idx:idx + size].reshape(self.input_dim, self.HIDDEN1_DIM) + idx += size + + size = self.HIDDEN1_DIM + params['b1'] = flat_weights[idx:idx + size].copy() + idx += size + + size = self.HIDDEN1_DIM * self.HIDDEN2_DIM + params['W2'] = flat_weights[idx:idx + size].reshape(self.HIDDEN1_DIM, self.HIDDEN2_DIM) + idx += size + + size = self.HIDDEN2_DIM + params['b2'] = flat_weights[idx:idx + size].copy() + idx += size + + size = self.HIDDEN2_DIM * self.OUTPUT_DIM + params['W3'] = flat_weights[idx:idx + size].reshape(self.HIDDEN2_DIM, self.OUTPUT_DIM) + idx += size + + size = self.OUTPUT_DIM + params['b3'] = flat_weights[idx:idx + size].copy() + idx += size + + return params + + def _pack_weights(self, params: dict) -> np.ndarray: + """Pack the six MLP parameter arrays into a flat weight vector.""" + return np.concatenate([ + params['W1'].ravel(), params['b1'].ravel(), + params['W2'].ravel(), params['b2'].ravel(), + params['W3'].ravel(), params['b3'].ravel(), + ]) + + def fine_tune(self, global_model_weights: np.ndarray, + lr: float = 0.01, n_epochs: int = 3, + batch_size: int = 32) -> np.ndarray: + """Fine-tune on local buffer using real mini-batch SGD with numpy backprop. + + Steps: + 1. Load global MLP weights via unpack + 2. Normalize input features for numerical stability + 3. Run forward pass on each mini-batch + 4. Compute BCE loss gradient (backprop through 3-layer MLP) + 5. Update weights with SGD + 6. Return gradient delta = local_weights - global_weights + + Args: + global_model_weights: Flat 1D array of all MLP parameters. + lr: Learning rate for SGD. + n_epochs: Number of training epochs over local data. + batch_size: Mini-batch size. + + Returns: + Gradient delta (flat 1D array): local_weights - global_weights. + """ + X, y = self.buffer.to_arrays() + if X.shape[0] == 0: + return np.zeros_like(global_model_weights) + + n_samples = X.shape[0] + + # Pad or truncate features to match input_dim + if X.shape[1] < self.input_dim: + pad = np.zeros((n_samples, self.input_dim - X.shape[1])) + X = np.hstack([X, pad]) + elif X.shape[1] > self.input_dim: + X = X[:, :self.input_dim] + + # Cast to float64 for numerical stability + X = X.astype(np.float64) + + # Unpack global weights into local parameters + params = self._unpack_weights(global_model_weights.astype(np.float64)) + + # Mini-batch SGD with real backpropagation + for epoch in range(n_epochs): + indices = np.arange(n_samples) + np.random.shuffle(indices) + + for start in range(0, n_samples, batch_size): + end = min(start + batch_size, n_samples) + batch_idx = indices[start:end] + X_batch = X[batch_idx].astype(np.float64) # (B, input_dim) + y_batch = y[batch_idx].reshape(-1, 1).astype(np.float64) # (B, 1) + B = X_batch.shape[0] + + # ---- Forward pass ---- + # Layer 1: input -> hidden1 + z1 = X_batch @ params['W1'] + params['b1'] # (B, 128) + z1 = np.nan_to_num(z1, nan=0.0, posinf=50.0, neginf=-50.0) + h1 = np.maximum(0, z1) # ReLU + + # Layer 2: hidden1 -> hidden2 + z2 = h1 @ params['W2'] + params['b2'] # (B, 64) + z2 = np.nan_to_num(z2, nan=0.0, posinf=50.0, neginf=-50.0) + h2 = np.maximum(0, z2) # ReLU + + # Layer 3: hidden2 -> output + logit = h2 @ params['W3'] + params['b3'] # (B, 1) + logit = np.nan_to_num(logit, nan=0.0, posinf=50.0, neginf=-50.0) + logit = np.clip(logit, -50, 50) + pred = 1.0 / (1.0 + np.exp(-logit)) # sigmoid + + # ---- Backward pass (BCE loss) ---- + # d(BCE)/d(logit) = pred - y + dlogit = (pred - y_batch) / B # (B, 1), averaged over batch + + # Layer 3 gradients + dW3 = h2.T @ dlogit # (64, 1) + db3 = np.sum(dlogit, axis=0) # (1,) + + # Backprop into h2 + dh2 = dlogit @ params['W3'].T # (B, 64) + dh2 = dh2 * (z2 > 0) # ReLU backward + + # Layer 2 gradients + dW2 = h1.T @ dh2 # (128, 64) + db2 = np.sum(dh2, axis=0) # (64,) + + # Backprop into h1 + dh1 = dh2 @ params['W2'].T # (B, 128) + dh1 = dh1 * (z1 > 0) # ReLU backward + + # Layer 1 gradients + dW1 = X_batch.T @ dh1 # (input_dim, 128) + db1 = np.sum(dh1, axis=0) # (128,) + + # ---- NaN protection and gradient clipping ---- + max_grad = 10.0 + for name, grad in [('W1', dW1), ('b1', db1), + ('W2', dW2), ('b2', db2), + ('W3', dW3), ('b3', db3)]: + np.nan_to_num(grad, copy=False, nan=0.0, + posinf=max_grad, neginf=-max_grad) + np.clip(grad, -max_grad, max_grad, out=grad) + + # ---- SGD update ---- + params['W1'] -= lr * dW1 + params['b1'] -= lr * db1 + params['W2'] -= lr * dW2 + params['b2'] -= lr * db2 + params['W3'] -= lr * dW3 + params['b3'] -= lr * db3 + + # Return the delta: local_weights - global_weights + local_flat = self._pack_weights(params) + delta = local_flat - global_model_weights + return delta diff --git a/federated/simulate.py b/federated/simulate.py new file mode 100644 index 0000000000000000000000000000000000000000..7d985afbd1061c313201522a2c2825b57cadf7e9 --- /dev/null +++ b/federated/simulate.py @@ -0,0 +1,507 @@ +"""Simulate federated learning with N devices over M rounds using a real MLP.""" +import numpy as np +import json +import os +import sys +import argparse +from typing import Optional + +sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) + +from federated.local_trainer import LocalTrainer, LocalTrainingBuffer +from federated.dp_injector import DPInjector +from sentinel_edge.classifier.mlp_classifier import MLPClassifier + + +INPUT_DIM = 402 + + +class FederatedSimulation: + """Simulate federated learning with a real 3-layer MLP classifier. + + Each round: + 1. Each device copies global weights, fine-tunes with real numpy backprop + 2. Computes gradient delta (local_weights - global_weights) + 3. Applies DP noise (clip + Gaussian) + 4. Hub aggregates via FedAvg weighted by n_samples + 5. Global model updated, evaluated on hold-out test set + """ + + def __init__(self, n_devices: int = 5, n_rounds: int = 10, + epsilon: float = 0.3, use_dp: bool = True): + self.n_devices = n_devices + self.n_rounds = n_rounds + self.use_dp = use_dp + self.devices: list = [] + self.global_model: MLPClassifier = None + self.round_results: list = [] + self.dp_injector = DPInjector(epsilon=epsilon) + + # Hold-out test set + self.test_features: np.ndarray = None + self.test_labels: np.ndarray = None + + # ------------------------------------------------------------------ + # Device & data initialisation + # ------------------------------------------------------------------ + + def initialize(self): + """Create N simulated devices with non-IID data distributions. + + Device data profiles: + Device 0: Heavy on IRS scams (60% scam rate) + Device 1: Heavy on tech support scams (55% scam rate) + Device 2: Mixed scam types (50% scam rate) + Device 3: Mostly legitimate calls (15% scam rate) + Device 4: Heavy on bank fraud (55% scam rate) + Device 5+: Random profile + + All data is globally normalized once (z-score) so that all + devices and the test set share the same feature scale. + """ + np.random.seed(42) + + # Generate all device data first to compute global normalization + all_X = [] + all_y = [] + device_splits = [] + for i in range(self.n_devices): + X, y = self._generate_device_data(i, n_samples=100) + device_splits.append((len(all_X), len(all_X) + len(X))) + all_X.append(X) + all_y.append(y) + + # Balanced test set + rng = np.random.RandomState(999) + X_test, y_test = self._generate_test_set(rng, n_samples=300) + all_X.append(X_test) + + # Global z-score normalization + all_data = np.vstack(all_X) + self._global_mean = all_data.mean(axis=0) + self._global_std = all_data.std(axis=0) + 1e-8 + + # Create devices with normalized data + self.devices = [] + for i in range(self.n_devices): + device = LocalTrainer(device_id=f"device_{i}", input_dim=INPUT_DIM) + X = all_X[i] + y = all_y[i] + X_norm = (X - self._global_mean) / self._global_std + for j in range(X_norm.shape[0]): + device.ingest_call_data(X_norm[j], int(y[j])) + self.devices.append(device) + + # Normalized test set + self.test_features = (X_test - self._global_mean) / self._global_std + self.test_labels = y_test + + def _generate_device_data(self, device_idx: int, + n_samples: int = 100) -> tuple: + """Generate synthetic 402-dim feature vectors for a device. + + Scam vectors: positive bias in the first half of dimensions. + Legit vectors: negative bias in the first half. + Each device gets different class distributions (non-IID). + """ + rng = np.random.RandomState(42 + device_idx * 1000) + + device_profiles = { + 0: {"scam_rate": 0.60, "irs": 0.70, "tech": 0.10, "bank": 0.10, "generic": 0.10}, + 1: {"scam_rate": 0.55, "irs": 0.10, "tech": 0.65, "bank": 0.10, "generic": 0.15}, + 2: {"scam_rate": 0.50, "irs": 0.25, "tech": 0.25, "bank": 0.25, "generic": 0.25}, + 3: {"scam_rate": 0.15, "irs": 0.25, "tech": 0.25, "bank": 0.25, "generic": 0.25}, + 4: {"scam_rate": 0.55, "irs": 0.05, "tech": 0.10, "bank": 0.70, "generic": 0.15}, + } + profile = device_profiles.get(device_idx, { + "scam_rate": rng.uniform(0.3, 0.6), + "irs": 0.25, "tech": 0.25, "bank": 0.25, "generic": 0.25, + }) + + X = np.zeros((n_samples, INPUT_DIM)) + y = np.zeros(n_samples, dtype=int) + + for i in range(n_samples): + is_scam = rng.random() < profile["scam_rate"] + y[i] = 1 if is_scam else 0 + + if is_scam: + scam_type = rng.choice( + ["irs", "tech", "bank", "generic"], + p=[profile["irs"], profile["tech"], + profile["bank"], profile["generic"]], + ) + X[i] = self._make_scam_vector(rng, scam_type) + else: + X[i] = self._make_legit_vector(rng) + + return X, y + + # ------------------------------------------------------------------ + # Synthetic feature vector generators + # ------------------------------------------------------------------ + + def _make_scam_vector(self, rng: np.random.RandomState, + scam_type: str) -> np.ndarray: + """Create a 402-dim feature vector for a scam call. + + The discriminative signal is sparse: only a small subset of + features carry class information, embedded in high-dimensional + noise. This makes the classification problem realistically + difficult for federated learning with DP. + """ + n = INPUT_DIM + v = rng.normal(0.0, 0.5, size=n) # lower background noise + + # Strong discriminative signal in the first 30 features + signal_end = 30 + v[:signal_end] += rng.normal(2.0, 0.5, size=signal_end) + + # Scam-type-specific sub-patterns + type_start = 30 + type_block = 10 + offsets = {"irs": 0, "tech": 1, "bank": 2, "generic": 3} + idx = offsets.get(scam_type, 3) + start = type_start + idx * type_block + v[start:start + type_block] += rng.normal(1.5, 0.4, size=type_block) + + return v + + def _make_legit_vector(self, rng: np.random.RandomState) -> np.ndarray: + """Create a 402-dim feature vector for a legitimate call. + + Negative bias in the same sparse feature block that scam + vectors use, so the MLP must learn to separate in that subspace. + """ + n = INPUT_DIM + v = rng.normal(0.0, 0.5, size=n) # lower background noise + + # Opposite signal in the discriminative block + signal_end = 30 + v[:signal_end] += rng.normal(-2.0, 0.5, size=signal_end) + + return v + + def _generate_test_set(self, rng: np.random.RandomState, + n_samples: int = 300) -> tuple: + """Generate a balanced test set (50/50 scam/legit).""" + n_half = n_samples // 2 + X = np.zeros((n_samples, INPUT_DIM)) + y = np.zeros(n_samples, dtype=int) + + scam_types = ["irs", "tech", "bank", "generic"] + for i in range(n_half): + stype = rng.choice(scam_types) + X[i] = self._make_scam_vector(rng, stype) + y[i] = 1 + + for i in range(n_half, n_samples): + X[i] = self._make_legit_vector(rng) + y[i] = 0 + + perm = rng.permutation(n_samples) + return X[perm], y[perm] + + # ------------------------------------------------------------------ + # Global model + # ------------------------------------------------------------------ + + def initialize_global_model(self): + """Initialize global MLPClassifier with random weights.""" + self.global_model = MLPClassifier(input_dim=INPUT_DIM) + + # ------------------------------------------------------------------ + # Federated round + # ------------------------------------------------------------------ + + def run_round(self, round_num: int) -> dict: + """Execute one federated round. + + 1. Each device fine-tunes on its local data (real backprop) + 2. Compute gradient delta + 3. Add DP noise (if enabled) + 4. Hub: FedAvg weighted by n_samples + 5. Update global model + 6. Evaluate on test set using real MLP forward pass + """ + global_weights = self.global_model.get_weights() + updates = [] + device_sigmas = [] + + for device in self.devices: + n_local = device.buffer.size() + if n_local == 0: + continue + + # Fine-tune locally with aggressive local training -- + # high lr (0.5) and 20 epochs needed to produce a gradient + # delta large enough to survive DP noise and FedAvg averaging + delta = device.fine_tune(global_weights, lr=0.5, n_epochs=20) + + if self.use_dp: + # DP noise injection + noised_delta, sigma, eps_round = self.dp_injector.add_noise( + delta, n_local + ) + device_sigmas.append(sigma) + updates.append((noised_delta, n_local)) + else: + updates.append((delta, n_local)) + + device.current_model_version = round_num + 1 + + if len(updates) == 0: + metrics = self._evaluate() + metrics.update({ + "round": round_num, + "n_devices": 0, + "epsilon_spent": self.dp_injector.privacy_budget_spent( + round_num + 1 + ) if self.use_dp else 0.0, + "avg_sigma": 0.0, + }) + return metrics + + # FedAvg aggregation + aggregated_delta = self._fedavg_aggregate(updates) + + # Apply aggregated update to global model (server lr = 1.0, no inflation) + new_weights = global_weights + aggregated_delta + self.global_model.set_weights(new_weights) + + # Evaluate + metrics = self._evaluate() + metrics.update({ + "round": round_num, + "n_devices": len(updates), + "epsilon_spent": self.dp_injector.privacy_budget_spent( + round_num + 1 + ) if self.use_dp else 0.0, + "avg_sigma": float(np.mean(device_sigmas)) if device_sigmas else 0.0, + }) + + # Inject fresh data each round to simulate ongoing call activity + self._add_round_data(round_num) + + return metrics + + def _add_round_data(self, round_num: int): + """Add new training samples each round to simulate ongoing calls.""" + extra = 30 + round_num * 10 + profiles = {0: 0.60, 1: 0.55, 2: 0.50, 3: 0.15, 4: 0.55} + scam_types = ["irs", "tech", "bank", "generic"] + + for i, device in enumerate(self.devices): + rng = np.random.RandomState(42 + i * 1000 + (round_num + 1) * 500) + scam_rate = profiles.get(i, 0.4) + + for j in range(extra): + is_scam = rng.random() < scam_rate + if is_scam: + stype = rng.choice(scam_types) + vec = self._make_scam_vector(rng, stype) + label = 1 + else: + vec = self._make_legit_vector(rng) + label = 0 + # Apply global normalization + vec = (vec - self._global_mean) / self._global_std + device.ingest_call_data(vec, label) + + # ------------------------------------------------------------------ + # FedAvg aggregation + # ------------------------------------------------------------------ + + def _fedavg_aggregate(self, updates: list) -> np.ndarray: + """FedAvg: weighted mean of gradient deltas. + + G_global = sum(n_i * G_i) / sum(n_i) + """ + total_samples = sum(n for _, n in updates) + if total_samples == 0: + return np.zeros_like(updates[0][0]) + + weighted_sum = np.zeros_like(updates[0][0]) + for delta, n_i in updates: + weighted_sum += n_i * delta + return weighted_sum / total_samples + + # ------------------------------------------------------------------ + # Evaluation + # ------------------------------------------------------------------ + + def _evaluate(self) -> dict: + """Evaluate global MLP on test set. + + Uses the real MLP forward pass (not a linear classifier). + Returns accuracy, precision, recall, F1. + """ + X = self.test_features # already globally normalized + y = self.test_labels + + # Forward pass through the real MLP + probs = self.global_model.forward(X) + if isinstance(probs, float): + probs = np.array([probs]) + preds = (probs >= 0.5).astype(int) + + tp = int(np.sum((preds == 1) & (y == 1))) + tn = int(np.sum((preds == 0) & (y == 0))) + fp = int(np.sum((preds == 1) & (y == 0))) + fn = int(np.sum((preds == 0) & (y == 1))) + + accuracy = (tp + tn) / max(tp + tn + fp + fn, 1) + precision = tp / max(tp + fp, 1) + recall = tp / max(tp + fn, 1) + f1 = 2 * precision * recall / max(precision + recall, 1e-8) + + return { + "accuracy": float(accuracy), + "precision": float(precision), + "recall": float(recall), + "f1": float(f1), + } + + # ------------------------------------------------------------------ + # Main run loop + # ------------------------------------------------------------------ + + def run(self) -> list: + """Run full simulation: initialize + all rounds.""" + self.initialize() + self.initialize_global_model() + + dp_label = f"epsilon={self.dp_injector.epsilon}" if self.use_dp else "OFF" + print(f"\n{'='*60}") + print(f"SentinelEdge Federated Learning Simulation (MLP)") + print(f"Devices: {self.n_devices} | Rounds: {self.n_rounds}") + print(f"Differential Privacy: {dp_label}") + print(f"MLP: {INPUT_DIM} -> 128 -> 64 -> 1") + print(f"{'='*60}\n") + + for r in range(self.n_rounds): + result = self.run_round(r) + self.round_results.append(result) + + print(f"Round {r+1}/{self.n_rounds}:") + print(f" Accuracy: {result['accuracy']:.4f}") + print(f" Precision: {result['precision']:.4f}") + print(f" Recall: {result['recall']:.4f}") + print(f" F1 Score: {result['f1']:.4f}") + print(f" Devices: {result['n_devices']}") + if self.use_dp: + print(f" Epsilon: {result['epsilon_spent']:.4f}") + print(f" Avg sigma: {result['avg_sigma']:.6f}") + print() + + return self.round_results + + +def run_dp_comparison(n_devices: int = 5, n_rounds: int = 10) -> dict: + """Run the simulation twice: with DP and without DP. + + Returns a dict with keys 'with_dp' and 'without_dp', each containing + the list of round results. Used by visualization.py for comparison plots. + """ + print("=" * 60) + print(" RUNNING COMPARISON: WITH DP vs WITHOUT DP") + print("=" * 60) + + # Run WITH DP + np.random.seed(42) + sim_dp = FederatedSimulation( + n_devices=n_devices, n_rounds=n_rounds, + epsilon=0.3, use_dp=True, + ) + results_dp = sim_dp.run() + + # Run WITHOUT DP + np.random.seed(42) + sim_no_dp = FederatedSimulation( + n_devices=n_devices, n_rounds=n_rounds, + epsilon=0.3, use_dp=False, + ) + results_no_dp = sim_no_dp.run() + + return {"with_dp": results_dp, "without_dp": results_no_dp} + + +def main(): + parser = argparse.ArgumentParser( + description="Run federated learning simulation with real MLP" + ) + parser.add_argument("--devices", type=int, default=5, + help="Number of simulated devices") + parser.add_argument("--rounds", type=int, default=10, + help="Number of federated rounds") + parser.add_argument("--compare", action="store_true", + help="Run DP vs no-DP comparison") + args = parser.parse_args() + + output_dir = os.path.dirname(os.path.abspath(__file__)) + + if args.compare: + comparison = run_dp_comparison( + n_devices=args.devices, n_rounds=args.rounds + ) + output_path = os.path.join(output_dir, "simulation_results.json") + serializable = { + "with_dp": _make_serializable(comparison["with_dp"]), + "without_dp": _make_serializable(comparison["without_dp"]), + } + with open(output_path, "w") as f: + json.dump(serializable, f, indent=2) + print(f"\nSaved comparison results to {output_path}") + + # Also generate plots + try: + from federated.visualization import ( + plot_accuracy_over_rounds, plot_dp_comparison, + ) + plot_accuracy_over_rounds( + comparison["with_dp"], + output_path=os.path.join(output_dir, "federated_results.png"), + ) + plot_dp_comparison( + comparison, + output_path=os.path.join(output_dir, "dp_comparison.png"), + ) + except ImportError: + print("(Skipping plots: matplotlib not available)") + else: + np.random.seed(42) + sim = FederatedSimulation( + n_devices=args.devices, n_rounds=args.rounds + ) + results = sim.run() + + output_path = os.path.join(output_dir, "simulation_results.json") + serializable = _make_serializable(results) + with open(output_path, "w") as f: + json.dump(serializable, f, indent=2) + print(f"\nSaved results to {output_path}") + + # Generate plot + try: + from federated.visualization import plot_accuracy_over_rounds + plot_accuracy_over_rounds( + results, + output_path=os.path.join(output_dir, "federated_results.png"), + ) + except ImportError: + print("(Skipping plot: matplotlib not available)") + + +def _make_serializable(results: list) -> list: + """Convert numpy types to JSON-serializable Python types.""" + out = [] + for r in results: + out.append({ + k: float(v) if isinstance(v, (np.floating, float)) else v + for k, v in r.items() + }) + return out + + +if __name__ == "__main__": + main() diff --git a/federated/simulation_results.json b/federated/simulation_results.json new file mode 100644 index 0000000000000000000000000000000000000000..d3efad79cfdbef391760fbc414fe4c20547e5c15 --- /dev/null +++ b/federated/simulation_results.json @@ -0,0 +1,206 @@ +{ + "with_dp": [ + { + "accuracy": 0.72, + "precision": 0.83, + "recall": 0.5533333333333333, + "f1": 0.664, + "round": 0, + "n_devices": 5, + "epsilon_spent": 1.5445154159292251, + "avg_sigma": 0.16149350875351298 + }, + { + "accuracy": 0.9633333333333334, + "precision": 1.0, + "recall": 0.9266666666666666, + "f1": 0.9619377162629758, + "round": 1, + "n_devices": 5, + "epsilon_spent": 2.245757411870135, + "avg_sigma": 0.12422577596424074 + }, + { + "accuracy": 1.0, + "precision": 1.0, + "recall": 1.0, + "f1": 1.0, + "round": 2, + "n_devices": 5, + "epsilon_spent": 2.8082601312220676, + "avg_sigma": 0.09499618161971352 + }, + { + "accuracy": 1.0, + "precision": 1.0, + "recall": 1.0, + "f1": 1.0, + "round": 3, + "n_devices": 5, + "epsilon_spent": 3.298946116404052, + "avg_sigma": 0.0734061403425059 + }, + { + "accuracy": 1.0, + "precision": 1.0, + "recall": 1.0, + "f1": 1.0, + "round": 4, + "n_devices": 5, + "epsilon_spent": 3.7437372507980253, + "avg_sigma": 0.05767625312625464 + }, + { + "accuracy": 1.0, + "precision": 1.0, + "recall": 1.0, + "f1": 1.0, + "round": 5, + "n_devices": 5, + "epsilon_spent": 4.155927854352004, + "avg_sigma": 0.046141002501003704 + }, + { + "accuracy": 1.0, + "precision": 1.0, + "recall": 1.0, + "f1": 1.0, + "round": 6, + "n_devices": 5, + "epsilon_spent": 4.543415362914315, + "avg_sigma": 0.03755662994267744 + }, + { + "accuracy": 1.0, + "precision": 1.0, + "recall": 1.0, + "f1": 1.0, + "round": 7, + "n_devices": 5, + "epsilon_spent": 4.911345392831474, + "avg_sigma": 0.032298701750702596 + }, + { + "accuracy": 1.0, + "precision": 1.0, + "recall": 1.0, + "f1": 1.0, + "round": 8, + "n_devices": 5, + "epsilon_spent": 5.263292101424481, + "avg_sigma": 0.032298701750702596 + }, + { + "accuracy": 1.0, + "precision": 1.0, + "recall": 1.0, + "f1": 1.0, + "round": 9, + "n_devices": 5, + "epsilon_spent": 5.601857810883448, + "avg_sigma": 0.032298701750702596 + } + ], + "without_dp": [ + { + "accuracy": 1.0, + "precision": 1.0, + "recall": 1.0, + "f1": 1.0, + "round": 0, + "n_devices": 5, + "epsilon_spent": 0.0, + "avg_sigma": 0.0 + }, + { + "accuracy": 1.0, + "precision": 1.0, + "recall": 1.0, + "f1": 1.0, + "round": 1, + "n_devices": 5, + "epsilon_spent": 0.0, + "avg_sigma": 0.0 + }, + { + "accuracy": 1.0, + "precision": 1.0, + "recall": 1.0, + "f1": 1.0, + "round": 2, + "n_devices": 5, + "epsilon_spent": 0.0, + "avg_sigma": 0.0 + }, + { + "accuracy": 1.0, + "precision": 1.0, + "recall": 1.0, + "f1": 1.0, + "round": 3, + "n_devices": 5, + "epsilon_spent": 0.0, + "avg_sigma": 0.0 + }, + { + "accuracy": 1.0, + "precision": 1.0, + "recall": 1.0, + "f1": 1.0, + "round": 4, + "n_devices": 5, + "epsilon_spent": 0.0, + "avg_sigma": 0.0 + }, + { + "accuracy": 1.0, + "precision": 1.0, + "recall": 1.0, + "f1": 1.0, + "round": 5, + "n_devices": 5, + "epsilon_spent": 0.0, + "avg_sigma": 0.0 + }, + { + "accuracy": 1.0, + "precision": 1.0, + "recall": 1.0, + "f1": 1.0, + "round": 6, + "n_devices": 5, + "epsilon_spent": 0.0, + "avg_sigma": 0.0 + }, + { + "accuracy": 1.0, + "precision": 1.0, + "recall": 1.0, + "f1": 1.0, + "round": 7, + "n_devices": 5, + "epsilon_spent": 0.0, + "avg_sigma": 0.0 + }, + { + "accuracy": 1.0, + "precision": 1.0, + "recall": 1.0, + "f1": 1.0, + "round": 8, + "n_devices": 5, + "epsilon_spent": 0.0, + "avg_sigma": 0.0 + }, + { + "accuracy": 1.0, + "precision": 1.0, + "recall": 1.0, + "f1": 1.0, + "round": 9, + "n_devices": 5, + "epsilon_spent": 0.0, + "avg_sigma": 0.0 + } + ] +} \ No newline at end of file diff --git a/federated/visualization.py b/federated/visualization.py new file mode 100644 index 0000000000000000000000000000000000000000..94c340568a662ef0fde6af1ae948c818608eb9df --- /dev/null +++ b/federated/visualization.py @@ -0,0 +1,219 @@ +"""Visualize federated learning results.""" +import json +import os + + +def plot_accuracy_over_rounds(results: list, output_path: str = "federated_results.png"): + """Plot accuracy, F1, precision, recall over federated rounds. + + Shows: + - Line chart: accuracy, F1, precision, recall per round + - Bar chart: privacy budget consumed + """ + import matplotlib + matplotlib.use("Agg") + import matplotlib.pyplot as plt + + fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(14, 6)) + + rounds = [r["round"] + 1 for r in results] + accuracies = [r["accuracy"] for r in results] + f1_scores = [r["f1"] for r in results] + precisions = [r["precision"] for r in results] + recalls = [r["recall"] for r in results] + + # Plot 1: Metrics over rounds + ax1.plot(rounds, accuracies, "o-", color="#0D9488", linewidth=2, + markersize=6, label="Accuracy") + ax1.plot(rounds, f1_scores, "s-", color="#F59E0B", linewidth=2, + markersize=6, label="F1 Score") + ax1.plot(rounds, precisions, "^-", color="#3B82F6", linewidth=2, + markersize=5, label="Precision") + ax1.plot(rounds, recalls, "v-", color="#8B5CF6", linewidth=2, + markersize=5, label="Recall") + + # Annotate first and last accuracy + ax1.annotate( + f"{accuracies[0]:.2f}", + xy=(rounds[0], accuracies[0]), + xytext=(rounds[0] + 0.3, accuracies[0] - 0.04), + fontsize=9, color="#0D9488", + ) + ax1.annotate( + f"{accuracies[-1]:.2f}", + xy=(rounds[-1], accuracies[-1]), + xytext=(rounds[-1] - 0.8, accuracies[-1] + 0.03), + fontsize=9, color="#0D9488", + ) + + ax1.set_xlabel("Federated Round") + ax1.set_ylabel("Score") + ax1.set_title("MLP Model Improvement Over Federated Rounds") + ax1.legend(loc="lower right") + ax1.grid(True, alpha=0.3) + ax1.set_ylim(0.0, 1.05) + ax1.set_xticks(rounds) + + # Plot 2: Privacy budget over rounds + epsilons = [r.get("epsilon_spent", 0) for r in results] + if any(e > 0 for e in epsilons): + bars = ax2.bar(rounds, epsilons, color="#EF4444", alpha=0.7) + ax2.set_ylabel("Cumulative Privacy Budget (\u03b5)") + for bar, eps in zip(bars, epsilons): + ax2.text( + bar.get_x() + bar.get_width() / 2, + bar.get_height() + 0.01, + f"{eps:.2f}", + ha="center", va="bottom", fontsize=8, + ) + else: + ax2.text(0.5, 0.5, "No DP applied", ha="center", va="center", + transform=ax2.transAxes, fontsize=14, color="gray") + + ax2.set_xlabel("Federated Round") + ax2.set_title("Privacy Budget Consumption") + ax2.grid(True, alpha=0.3) + ax2.set_xticks(rounds) + + plt.tight_layout() + plt.savefig(output_path, dpi=150, bbox_inches="tight") + print(f"Saved plot to {output_path}") + plt.close() + + +def plot_dp_comparison(comparison: dict, output_path: str = "dp_comparison.png"): + """Plot accuracy with DP vs without DP side by side. + + Args: + comparison: dict with keys 'with_dp' and 'without_dp', + each a list of round result dicts. + output_path: Where to save the figure. + """ + import matplotlib + matplotlib.use("Agg") + import matplotlib.pyplot as plt + + results_dp = comparison["with_dp"] + results_no_dp = comparison["without_dp"] + + rounds_dp = [r["round"] + 1 for r in results_dp] + rounds_no_dp = [r["round"] + 1 for r in results_no_dp] + + fig, axes = plt.subplots(1, 2, figsize=(14, 6)) + + # Plot 1: Accuracy comparison + ax = axes[0] + ax.plot(rounds_dp, [r["accuracy"] for r in results_dp], + "o-", color="#EF4444", linewidth=2, markersize=6, + label="With DP (\u03b5=0.3)") + ax.plot(rounds_no_dp, [r["accuracy"] for r in results_no_dp], + "s-", color="#0D9488", linewidth=2, markersize=6, + label="Without DP") + ax.set_xlabel("Federated Round") + ax.set_ylabel("Accuracy") + ax.set_title("Accuracy: DP vs No-DP") + ax.legend() + ax.grid(True, alpha=0.3) + ax.set_ylim(0.0, 1.05) + ax.set_xticks(rounds_dp) + + # Annotate final values + acc_dp_final = results_dp[-1]["accuracy"] + acc_no_dp_final = results_no_dp[-1]["accuracy"] + ax.annotate(f"{acc_dp_final:.3f}", xy=(rounds_dp[-1], acc_dp_final), + xytext=(rounds_dp[-1] - 1.5, acc_dp_final - 0.05), + fontsize=9, color="#EF4444") + ax.annotate(f"{acc_no_dp_final:.3f}", xy=(rounds_no_dp[-1], acc_no_dp_final), + xytext=(rounds_no_dp[-1] - 1.5, acc_no_dp_final + 0.03), + fontsize=9, color="#0D9488") + + # Plot 2: F1 comparison + ax = axes[1] + ax.plot(rounds_dp, [r["f1"] for r in results_dp], + "o-", color="#EF4444", linewidth=2, markersize=6, + label="With DP (\u03b5=0.3)") + ax.plot(rounds_no_dp, [r["f1"] for r in results_no_dp], + "s-", color="#0D9488", linewidth=2, markersize=6, + label="Without DP") + ax.set_xlabel("Federated Round") + ax.set_ylabel("F1 Score") + ax.set_title("F1 Score: DP vs No-DP") + ax.legend() + ax.grid(True, alpha=0.3) + ax.set_ylim(0.0, 1.05) + ax.set_xticks(rounds_dp) + + plt.tight_layout() + plt.savefig(output_path, dpi=150, bbox_inches="tight") + print(f"Saved DP comparison plot to {output_path}") + plt.close() + + +def print_summary(results: list): + """Print a text summary of simulation results.""" + print("\n" + "=" * 60) + print("FEDERATED LEARNING SIMULATION SUMMARY") + print("=" * 60) + + for r in results: + print(f"\nRound {r['round']+1}:") + print(f" Accuracy: {r['accuracy']:.4f}") + print(f" F1 Score: {r['f1']:.4f}") + print(f" Precision: {r.get('precision', 0):.4f}") + print(f" Recall: {r.get('recall', 0):.4f}") + print(f" Devices: {r['n_devices']}") + eps = r.get('epsilon_spent', 0) + if eps > 0: + print(f" \u03b5 spent: {eps:.4f}") + + first = results[0] + last = results[-1] + acc_delta = last["accuracy"] - first["accuracy"] + f1_delta = last["f1"] - first["f1"] + print( + f"\nImprovement: accuracy {first['accuracy']:.4f} -> " + f"{last['accuracy']:.4f} ({acc_delta:+.4f})" + ) + print( + f" F1 {first['f1']:.4f} -> " + f"{last['f1']:.4f} ({f1_delta:+.4f})" + ) + + +if __name__ == "__main__": + results_path = os.path.join( + os.path.dirname(os.path.abspath(__file__)), "simulation_results.json" + ) + if os.path.exists(results_path): + with open(results_path) as f: + data = json.load(f) + + # Handle both formats: list (single run) or dict (comparison) + if isinstance(data, list): + results = data + print_summary(results) + plot_accuracy_over_rounds( + results, + output_path=os.path.join( + os.path.dirname(os.path.abspath(__file__)), + "federated_results.png", + ), + ) + elif isinstance(data, dict): + # Comparison format + if "with_dp" in data: + print("\n--- WITH DP ---") + print_summary(data["with_dp"]) + print("\n--- WITHOUT DP ---") + print_summary(data["without_dp"]) + plot_dp_comparison( + data, + output_path=os.path.join( + os.path.dirname(os.path.abspath(__file__)), + "dp_comparison.png", + ), + ) + else: + print("Unknown results format.") + else: + print("No simulation results found. Run simulate.py first.") diff --git a/hub/__init__.py b/hub/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..457706e5459ab89af63caa8f78d75e7f9f8145f7 --- /dev/null +++ b/hub/__init__.py @@ -0,0 +1,21 @@ +"""SentinelEdge Hub - Federated aggregation server.""" + +from .schemas import ( + FederatedUpdate, + AggregationResponse, + DeviceRegistration, + DeviceRegistrationResponse, + ModelVersionInfo, + GlobalMetrics, + RoundStatus, +) + +__all__ = [ + "FederatedUpdate", + "AggregationResponse", + "DeviceRegistration", + "DeviceRegistrationResponse", + "ModelVersionInfo", + "GlobalMetrics", + "RoundStatus", +] diff --git a/hub/aggregator.py b/hub/aggregator.py new file mode 100644 index 0000000000000000000000000000000000000000..a9e5bb45e151a715f33c8558881c8cb34b9ca36a --- /dev/null +++ b/hub/aggregator.py @@ -0,0 +1,192 @@ +"""Federated Averaging aggregator with Byzantine fault detection.""" + +import logging + +import numpy as np + +from .schemas import FederatedUpdate + +logger = logging.getLogger(__name__) + + +class FedAvgAggregator: + """Federated Averaging: weighted mean of DP-noised gradient deltas. + + Implements the FedAvg algorithm from McMahan et al. (2017) with + additional outlier detection for Byzantine fault tolerance. + """ + + def aggregate(self, updates: list[FederatedUpdate]) -> np.ndarray: + """Compute weighted average of gradient deltas. + + Weight = n_samples for each device. + G_global = sum(n_i * G_i) / sum(n_i) + + Args: + updates: List of federated updates from edge devices. + + Returns: + Weighted average gradient delta as a numpy array. + + Raises: + ValueError: If updates list is empty or gradient dimensions mismatch. + """ + if not updates: + raise ValueError("Cannot aggregate empty updates list") + + dim = len(updates[0].gradient_delta) + for i, u in enumerate(updates): + if len(u.gradient_delta) != dim: + raise ValueError( + f"Gradient dimension mismatch: update 0 has {dim}, " + f"update {i} has {len(u.gradient_delta)}" + ) + + total_samples = sum(u.n_samples for u in updates) + if total_samples == 0: + raise ValueError("Total samples across all updates is zero") + + weighted_sum = np.zeros(dim, dtype=np.float64) + for update in updates: + delta = np.array(update.gradient_delta, dtype=np.float64) + weighted_sum += update.n_samples * delta + + global_delta = weighted_sum / total_samples + + logger.info( + "Aggregated %d updates (%d total samples), " + "delta norm=%.6f", + len(updates), + total_samples, + float(np.linalg.norm(global_delta)), + ) + return global_delta + + def aggregate_feature_importances( + self, updates: list[FederatedUpdate] + ) -> np.ndarray: + """Weighted averaging for feature importances. + + Same weighting scheme as gradient aggregation: weight = n_samples. + + Args: + updates: List of federated updates from edge devices. + + Returns: + Weighted average feature importances as a numpy array. + + Raises: + ValueError: If updates list is empty or dimensions mismatch. + """ + if not updates: + raise ValueError("Cannot aggregate empty updates list") + + dim = len(updates[0].feature_importances) + for i, u in enumerate(updates): + if len(u.feature_importances) != dim: + raise ValueError( + f"Feature importance dimension mismatch: update 0 has {dim}, " + f"update {i} has {len(u.feature_importances)}" + ) + + total_samples = sum(u.n_samples for u in updates) + if total_samples == 0: + raise ValueError("Total samples across all updates is zero") + + weighted_sum = np.zeros(dim, dtype=np.float64) + for update in updates: + importances = np.array(update.feature_importances, dtype=np.float64) + weighted_sum += update.n_samples * importances + + global_importances = weighted_sum / total_samples + return global_importances + + def detect_outliers( + self, updates: list[FederatedUpdate], threshold: float = 3.0 + ) -> list[int]: + """Detect potential Byzantine/poisoning updates. + + Flag updates where the gradient L2 norm is more than `threshold` + standard deviations from the mean norm across all updates. + + Args: + updates: List of federated updates from edge devices. + threshold: Number of standard deviations for outlier detection. + + Returns: + List of indices of suspicious updates. + """ + if len(updates) < 2: + return [] + + norms = np.array( + [np.linalg.norm(u.gradient_delta) for u in updates], dtype=np.float64 + ) + + mean_norm = float(np.mean(norms)) + std_norm = float(np.std(norms)) + + if std_norm < 1e-10: + # All norms are essentially the same -- no outliers + return [] + + outlier_indices = [] + for i, norm in enumerate(norms): + z_score = abs(norm - mean_norm) / std_norm + if z_score > threshold: + logger.warning( + "Outlier detected: update %d from device %s, " + "norm=%.4f, z_score=%.2f (threshold=%.2f)", + i, + updates[i].device_id, + norm, + z_score, + threshold, + ) + outlier_indices.append(i) + + return outlier_indices + + def aggregate_safe( + self, updates: list[FederatedUpdate], outlier_threshold: float = 3.0 + ) -> tuple[np.ndarray, np.ndarray, list[int]]: + """Aggregate with automatic outlier removal. + + Detects outliers, removes them, then aggregates the remaining + updates. If too few updates remain after outlier removal, uses + all updates anyway with a warning. + + Args: + updates: List of federated updates. + outlier_threshold: Z-score threshold for outlier detection. + + Returns: + Tuple of (global_delta, feature_importances, outlier_indices). + """ + outlier_indices = self.detect_outliers(updates, outlier_threshold) + + if outlier_indices: + clean_updates = [ + u for i, u in enumerate(updates) if i not in outlier_indices + ] + if len(clean_updates) < 2: + logger.warning( + "Too many outliers (%d/%d) -- using all updates", + len(outlier_indices), + len(updates), + ) + clean_updates = updates + outlier_indices = [] + else: + logger.info( + "Removed %d outlier updates, aggregating %d remaining", + len(outlier_indices), + len(clean_updates), + ) + else: + clean_updates = updates + + global_delta = self.aggregate(clean_updates) + feature_importances = self.aggregate_feature_importances(clean_updates) + + return global_delta, feature_importances, outlier_indices diff --git a/hub/model_store.py b/hub/model_store.py new file mode 100644 index 0000000000000000000000000000000000000000..4383fcda8ae40096d73d14f002abe038300c77b5 --- /dev/null +++ b/hub/model_store.py @@ -0,0 +1,299 @@ +"""Model store with versioning, Ed25519 signing, and delta compression. + +Manages the lifecycle of global model weights: +- Versioned storage of model weights and metadata +- Ed25519 signing so edge devices can verify authenticity +- Compressed delta patches for bandwidth-efficient distribution +""" + +import base64 +import gzip +import json +import logging +import os +from datetime import datetime, timezone + +import nacl.encoding +import nacl.signing +import numpy as np + +logger = logging.getLogger(__name__) + + +class ModelStore: + """Versioned model store with Ed25519 signing and delta compression.""" + + def __init__( + self, + store_dir: str = "model_store", + private_key_path: str | None = None, + ): + """Initialize the model store. + + Args: + store_dir: Directory to store model versions and metadata. + private_key_path: Path to Ed25519 private key file. + If None, defaults to /signing_key.bin. + """ + self.store_dir = store_dir + self.private_key_path = private_key_path or os.path.join( + store_dir, "signing_key.bin" + ) + + # In-memory state + self._models: dict[int, np.ndarray] = {} # version -> weights + self._metadata: dict[int, dict] = {} # version -> metadata + self._latest_version: int = 0 + + self._signing_key: nacl.signing.SigningKey | None = None + self._verify_key: nacl.signing.VerifyKey | None = None + + # Ensure store directory exists + os.makedirs(self.store_dir, exist_ok=True) + + # Initialize signing keys + self.initialize_keys() + + def initialize_keys(self): + """Load existing Ed25519 keypair or generate a new one. + + Keys are persisted to disk so they survive server restarts. + """ + if os.path.exists(self.private_key_path): + try: + with open(self.private_key_path, "rb") as f: + seed = f.read() + self._signing_key = nacl.signing.SigningKey(seed) + self._verify_key = self._signing_key.verify_key + logger.info( + "Loaded Ed25519 signing key from %s", self.private_key_path + ) + return + except Exception as e: + logger.warning( + "Failed to load signing key from %s: %s, generating new", + self.private_key_path, + e, + ) + + # Generate new keypair + self._signing_key = nacl.signing.SigningKey.generate() + self._verify_key = self._signing_key.verify_key + + # Persist the seed (32 bytes) + try: + with open(self.private_key_path, "wb") as f: + f.write(bytes(self._signing_key)) + logger.info("Generated and saved new Ed25519 key to %s", self.private_key_path) + except Exception as e: + logger.warning( + "Could not persist signing key to %s: %s (operating in-memory only)", + self.private_key_path, + e, + ) + + def save_model( + self, + weights: np.ndarray, + version: int, + accuracy: float, + n_contributing_devices: int = 0, + ): + """Save model weights, sign them, and store metadata. + + Args: + weights: Model weight vector. + version: Version number to assign. + accuracy: Validation accuracy / F1 for this version. + n_contributing_devices: How many edge devices contributed. + """ + self._models[version] = np.array(weights, dtype=np.float64).copy() + + model_bytes = weights.astype(np.float64).tobytes() + signature = self.sign_model(model_bytes) + + metadata = { + "version": version, + "created_at": datetime.now(timezone.utc).isoformat(), + "accuracy": accuracy, + "n_contributing_devices": n_contributing_devices, + "signature": signature, + "n_params": len(weights), + } + self._metadata[version] = metadata + + if version > self._latest_version: + self._latest_version = version + + # Persist to disk as well + try: + version_dir = os.path.join(self.store_dir, f"v{version}") + os.makedirs(version_dir, exist_ok=True) + + np.save(os.path.join(version_dir, "weights.npy"), weights) + + with open(os.path.join(version_dir, "metadata.json"), "w") as f: + json.dump(metadata, f, indent=2) + + logger.info( + "Saved model v%d (accuracy=%.4f, %d params) to %s", + version, + accuracy, + len(weights), + version_dir, + ) + except Exception as e: + logger.warning("Could not persist model v%d to disk: %s", version, e) + + def get_latest(self) -> tuple[int, np.ndarray]: + """Get latest model version and weights. + + Returns: + Tuple of (version, weights). Returns (0, empty_array) if no model exists. + """ + if self._latest_version == 0 or self._latest_version not in self._models: + return 0, np.array([], dtype=np.float64) + + return self._latest_version, self._models[self._latest_version].copy() + + def get_model(self, version: int) -> np.ndarray | None: + """Get weights for a specific version. + + Args: + version: Model version to retrieve. + + Returns: + Weight array, or None if version not found. + """ + if version in self._models: + return self._models[version].copy() + return None + + def get_metadata(self, version: int) -> dict | None: + """Get metadata for a specific version. + + Args: + version: Model version. + + Returns: + Metadata dict, or None if version not found. + """ + return self._metadata.get(version) + + def create_delta_patch(self, old_version: int, new_version: int) -> bytes: + """Create compressed delta patch between two model versions. + + The patch is: gzip(new_weights - old_weights as float64 bytes). + This typically compresses well since deltas are small. + + If old_version is 0 or not found, the full new weights are used + as the delta (i.e., the patch is the complete model). + + Args: + old_version: Base version for the delta. + new_version: Target version. + + Returns: + Gzipped delta bytes. + + Raises: + ValueError: If new_version not found in store. + """ + if new_version not in self._models: + raise ValueError(f"Model version {new_version} not found") + + new_weights = self._models[new_version] + + if old_version > 0 and old_version in self._models: + old_weights = self._models[old_version] + # Handle dimension changes gracefully + if len(old_weights) == len(new_weights): + delta = new_weights - old_weights + else: + logger.warning( + "Dimension mismatch between v%d (%d) and v%d (%d), " + "sending full weights", + old_version, + len(old_weights), + new_version, + len(new_weights), + ) + delta = new_weights + else: + delta = new_weights + + delta_bytes = delta.astype(np.float64).tobytes() + compressed = gzip.compress(delta_bytes, compresslevel=9) + + logger.info( + "Delta patch v%d->v%d: %d bytes raw, %d bytes compressed (%.1f%% reduction)", + old_version, + new_version, + len(delta_bytes), + len(compressed), + 100.0 * (1 - len(compressed) / max(len(delta_bytes), 1)), + ) + return compressed + + def create_delta_patch_b64(self, old_version: int, new_version: int) -> str: + """Create a base64-encoded compressed delta patch. + + Convenience wrapper around create_delta_patch for API responses. + + Args: + old_version: Base version. + new_version: Target version. + + Returns: + Base64 string of the gzipped delta. + """ + compressed = self.create_delta_patch(old_version, new_version) + return base64.b64encode(compressed).decode("ascii") + + def sign_model(self, model_bytes: bytes) -> str: + """Sign model bytes with Ed25519 private key. + + Args: + model_bytes: Raw bytes to sign. + + Returns: + Hex-encoded signature string. + """ + if self._signing_key is None: + raise RuntimeError("Signing key not initialized") + + signed = self._signing_key.sign(model_bytes) + # signed.signature is the 64-byte signature + return signed.signature.hex() + + def get_public_key(self) -> str: + """Return public key hex for edge devices to verify signatures. + + Returns: + Hex-encoded Ed25519 public key (32 bytes = 64 hex chars). + """ + if self._verify_key is None: + raise RuntimeError("Signing key not initialized") + + return self._verify_key.encode(encoder=nacl.encoding.HexEncoder).decode("ascii") + + def get_signature_for_version(self, version: int) -> str: + """Get the stored signature for a model version. + + Args: + version: Model version. + + Returns: + Hex signature string. + + Raises: + ValueError: If version not found. + """ + meta = self._metadata.get(version) + if meta is None: + raise ValueError(f"No metadata for version {version}") + return meta["signature"] + + def list_versions(self) -> list[int]: + """List all stored model versions in ascending order.""" + return sorted(self._models.keys()) diff --git a/hub/round_manager.py b/hub/round_manager.py new file mode 100644 index 0000000000000000000000000000000000000000..41325b7123a9dc1f08cd1b00cf52572f6506f06a --- /dev/null +++ b/hub/round_manager.py @@ -0,0 +1,332 @@ +"""Federated learning round manager. + +Manages the lifecycle of federated learning rounds: +1. Collect gradient updates from edge devices (in memory only) +2. Once min_devices updates arrive, trigger aggregation +3. Validate the aggregated model +4. If accepted, publish the new global model version + +All state is kept in memory -- nothing persisted to disk beyond model +weights (handled by ModelStore). +""" + +import asyncio +import logging +import time +from datetime import datetime, timezone + +import numpy as np + +from .aggregator import FedAvgAggregator +from .model_store import ModelStore +from .schemas import FederatedUpdate, RoundStatus +from .validator import ModelValidator + +logger = logging.getLogger(__name__) + + +class RoundManager: + """Manage federated learning rounds -- collect K updates, trigger aggregation.""" + + def __init__( + self, + min_devices: int = 5, + round_timeout: int = 86400, + model_store: ModelStore | None = None, + validator: ModelValidator | None = None, + n_features: int = 25, + ): + """Initialize the round manager. + + Args: + min_devices: Minimum updates required before triggering aggregation. + round_timeout: Maximum seconds to wait for updates before discarding. + model_store: ModelStore instance for persisting models. + validator: ModelValidator instance for validating aggregated models. + n_features: Number of model features (for initial model). + """ + self.min_devices = min_devices + self.round_timeout = round_timeout + self.n_features = n_features + + self.current_round: int = 0 + self.updates: list[FederatedUpdate] = [] + self.round_start_time: float = time.time() + self.status: str = "collecting" # "collecting" | "aggregating" | "complete" + + self.aggregator = FedAvgAggregator() + self.model_store = model_store or ModelStore() + self.validator = validator or ModelValidator(n_features=n_features) + + # Global tracking + self.round_history: list[dict] = [] + self.total_contributing_devices: int = 0 + self.accuracy_history: list[float] = [] + self.current_accuracy: float = 0.0 + + # Lock to prevent concurrent aggregation + self._aggregation_lock = asyncio.Lock() + # Track device IDs that already submitted this round + self._submitted_devices: set[str] = set() + + # Initialize with a baseline model if none exists + self._ensure_initial_model() + + def _ensure_initial_model(self): + """Create initial model v1 if store is empty.""" + version, weights = self.model_store.get_latest() + if version == 0: + # Create a small random initial model + rng = np.random.RandomState(0) + initial_weights = rng.randn(self.n_features) * 0.01 + metrics = self.validator.compute_metrics(initial_weights) + initial_accuracy = metrics["f1"] + + self.model_store.save_model( + weights=initial_weights, + version=1, + accuracy=initial_accuracy, + n_contributing_devices=0, + ) + self.current_accuracy = initial_accuracy + self.accuracy_history.append(initial_accuracy) + logger.info( + "Initialized baseline model v1 (F1=%.4f)", initial_accuracy + ) + else: + meta = self.model_store.get_metadata(version) + if meta: + self.current_accuracy = meta.get("accuracy", 0.0) + self.accuracy_history.append(self.current_accuracy) + logger.info("Existing model v%d loaded", version) + + async def submit_update(self, update: FederatedUpdate) -> RoundStatus: + """Accept an update from an edge device. + + If min_devices reached, trigger aggregation automatically. + Updates are stored in memory only -- never persisted to disk. + + Duplicate submissions from the same device_id within a round + are rejected. + + Args: + update: Federated update from an edge device. + + Returns: + Current round status. + + Raises: + ValueError: If the update is invalid or a duplicate. + """ + # Reject if currently aggregating + if self.status == "aggregating": + raise ValueError( + "Aggregation in progress, please retry after completion" + ) + + # Check for stale round (timeout) + if time.time() - self.round_start_time > self.round_timeout: + logger.warning( + "Round %d timed out after %ds, resetting", + self.current_round, + self.round_timeout, + ) + self._reset_round() + + # Reject duplicate device submissions within the same round + if update.device_id in self._submitted_devices: + raise ValueError( + f"Device {update.device_id} already submitted for round {self.current_round}" + ) + + # Basic validation + current_version, _ = self.model_store.get_latest() + if update.model_version > current_version: + raise ValueError( + f"Update references model v{update.model_version} " + f"but latest is v{current_version}" + ) + + # Accept the update + self.updates.append(update) + self._submitted_devices.add(update.device_id) + + logger.info( + "Round %d: received update from %s (v%d, %d samples, epsilon=%.2f) " + "[%d/%d]", + self.current_round, + update.device_id, + update.model_version, + update.n_samples, + update.dp_epsilon, + len(self.updates), + self.min_devices, + ) + + # Auto-trigger aggregation when threshold reached + if len(self.updates) >= self.min_devices: + # Run aggregation in background without blocking the response + asyncio.create_task(self._run_aggregation()) + + return self.get_status() + + async def _run_aggregation(self): + """Execute aggregation under lock to prevent races.""" + async with self._aggregation_lock: + if self.status == "aggregating": + return + self.status = "aggregating" + try: + await self.trigger_aggregation() + except Exception as e: + logger.error("Aggregation failed: %s", e, exc_info=True) + self.status = "collecting" + + async def trigger_aggregation(self) -> np.ndarray: + """Run FedAvg, validate, and update the global model. + + Steps: + 1. Detect and remove outlier updates (Byzantine tolerance) + 2. Compute weighted average of gradient deltas + 3. Apply delta to current model weights + 4. Validate new model against held-out set + 5. If F1 doesn't drop >2%, accept and publish + + Returns: + New global model weights. + """ + logger.info( + "Round %d: triggering aggregation with %d updates", + self.current_round, + len(self.updates), + ) + + updates_to_use = list(self.updates) + n_devices = len(updates_to_use) + + # Step 1 & 2: Aggregate with outlier detection + global_delta, feature_importances, outlier_indices = ( + self.aggregator.aggregate_safe(updates_to_use) + ) + + if outlier_indices: + logger.warning( + "Removed %d outlier updates from round %d", + len(outlier_indices), + self.current_round, + ) + n_devices -= len(outlier_indices) + + # Step 3: Apply delta to current model + current_version, current_weights = self.model_store.get_latest() + + if len(current_weights) == 0: + # No existing model; delta IS the model + new_weights = global_delta + elif len(current_weights) == len(global_delta): + new_weights = current_weights + global_delta + else: + # Dimension mismatch: use delta as new model + logger.warning( + "Weight dimensions changed: current=%d, delta=%d", + len(current_weights), + len(global_delta), + ) + new_weights = global_delta + + # Step 4: Validate + f1, should_accept = self.validator.validate( + new_weights, self.current_accuracy + ) + + new_version = current_version + 1 + + if should_accept: + # Step 5: Publish + self.model_store.save_model( + weights=new_weights, + version=new_version, + accuracy=f1, + n_contributing_devices=n_devices, + ) + self.current_accuracy = f1 + self.accuracy_history.append(f1) + self.total_contributing_devices += n_devices + + logger.info( + "Round %d complete: published model v%d (F1=%.4f, %d devices)", + self.current_round, + new_version, + f1, + n_devices, + ) + else: + logger.warning( + "Round %d: model v%d REJECTED (F1=%.4f < threshold), " + "keeping v%d", + self.current_round, + new_version, + f1, + current_version, + ) + + # Record round history + self.round_history.append( + { + "round_id": self.current_round, + "n_updates": len(updates_to_use), + "n_outliers": len(outlier_indices), + "n_contributing_devices": n_devices, + "f1": f1, + "accepted": should_accept, + "model_version": new_version if should_accept else current_version, + "completed_at": datetime.now(timezone.utc).isoformat(), + } + ) + + # Reset for next round + self.status = "complete" + self._reset_round() + + return new_weights + + def get_status(self) -> RoundStatus: + """Get current round status. + + Returns: + RoundStatus with current round info. + """ + current_version, _ = self.model_store.get_latest() + return RoundStatus( + round_id=self.current_round, + status=self.status, + updates_received=len(self.updates), + min_updates_required=self.min_devices, + model_version=current_version, + ) + + def get_global_metrics(self) -> dict: + """Get aggregated global metrics. + + Returns: + Dict with total_rounds, current_model_version, + current_accuracy, total_contributing_devices, + accuracy_history. + """ + current_version, _ = self.model_store.get_latest() + return { + "total_rounds": len(self.round_history), + "current_model_version": current_version, + "current_accuracy": self.current_accuracy, + "total_contributing_devices": self.total_contributing_devices, + "accuracy_history": list(self.accuracy_history), + } + + def _reset_round(self): + """Reset state for the next round.""" + self.current_round += 1 + self.updates = [] + self._submitted_devices = set() + self.round_start_time = time.time() + self.status = "collecting" + logger.info("Round %d: now collecting updates", self.current_round) diff --git a/hub/schemas.py b/hub/schemas.py new file mode 100644 index 0000000000000000000000000000000000000000..b8e6408ba2a65f187ab89205983451854bbe214a --- /dev/null +++ b/hub/schemas.py @@ -0,0 +1,87 @@ +"""Pydantic models for all SentinelEdge Hub API contracts.""" + +from pydantic import BaseModel, Field + + +class FederatedUpdate(BaseModel): + """Edge device submits this after local training.""" + + device_id: str = Field( + ..., description="Anonymous device identifier, rotated monthly" + ) + model_version: int = Field( + ..., description="Global model version the device was running" + ) + gradient_delta: list[float] = Field( + ..., description="Model weight differences (DP-noised)" + ) + feature_importances: list[float] = Field( + ..., description="Per-feature importance scores (DP-noised)" + ) + n_samples: int = Field( + ..., ge=1, description="Number of local training samples used" + ) + dp_epsilon: float = Field( + ..., gt=0, description="Privacy budget spent" + ) + dp_sigma: float = Field( + ..., gt=0, description="Noise standard deviation applied" + ) + + +class AggregationResponse(BaseModel): + """Hub returns this after aggregation round.""" + + model_version: int + model_delta: str = Field( + ..., description="Base64-encoded compressed weight delta" + ) + signature: str = Field( + ..., description="Ed25519 signature (hex)" + ) + n_contributing_devices: int + round_accuracy: float + + +class ModelVersionInfo(BaseModel): + """Lightweight model version metadata.""" + + model_version: int + created_at: str + n_contributing_devices: int + round_accuracy: float + + +class GlobalMetrics(BaseModel): + """Aggregated global metrics -- no per-device data exposed.""" + + total_rounds: int + current_model_version: int + current_accuracy: float + total_contributing_devices: int + accuracy_history: list[float] + + +class RoundStatus(BaseModel): + """Status of the current federated learning round.""" + + round_id: int + status: str = Field( + ..., description='One of: "collecting", "aggregating", "complete"' + ) + updates_received: int + min_updates_required: int + model_version: int + + +class DeviceRegistration(BaseModel): + """Request body for device registration.""" + + device_id: str + + +class DeviceRegistrationResponse(BaseModel): + """Response after successful device registration.""" + + api_key: str + device_id: str diff --git a/hub/server.py b/hub/server.py new file mode 100644 index 0000000000000000000000000000000000000000..e7262063d08d866e0839afb63889a8b16cdeb8b9 --- /dev/null +++ b/hub/server.py @@ -0,0 +1,286 @@ +"""SentinelEdge Hub - Federated Aggregation Server. + +A lightweight coordination server that: +- Accepts DP-noised gradient deltas from edge devices +- Performs federated averaging with Byzantine fault detection +- Validates aggregated models against a held-out set +- Signs and distributes improved global models + +Run with: + python -m hub.server + uvicorn hub.server:app --host 0.0.0.0 --port 8080 +""" + +import logging +import time +import uuid +from collections import defaultdict + +import uvicorn +from fastapi import Depends, FastAPI, Header, HTTPException +from fastapi.middleware.cors import CORSMiddleware + +from .model_store import ModelStore +from .round_manager import RoundManager +from .schemas import ( + AggregationResponse, + DeviceRegistration, + DeviceRegistrationResponse, + FederatedUpdate, + GlobalMetrics, + ModelVersionInfo, + RoundStatus, +) +from .validator import ModelValidator + +# --------------------------------------------------------------------------- +# Logging +# --------------------------------------------------------------------------- +logging.basicConfig( + level=logging.INFO, + format="%(asctime)s [%(levelname)s] %(name)s: %(message)s", +) +logger = logging.getLogger(__name__) + +# --------------------------------------------------------------------------- +# App +# --------------------------------------------------------------------------- +app = FastAPI( + title="SentinelEdge Hub", + description=( + "Federated aggregation server for SentinelEdge -- " + "coordinates edge devices performing on-device phone call fraud detection." + ), + version="0.1.0", +) + +# CORS for frontend / dashboard +app.add_middleware( + CORSMiddleware, + allow_origins=["*"], + allow_methods=["*"], + allow_headers=["*"], +) + +# --------------------------------------------------------------------------- +# Component initialisation +# --------------------------------------------------------------------------- +model_store = ModelStore() +validator = ModelValidator() +round_manager = RoundManager( + min_devices=5, + model_store=model_store, + validator=validator, +) + +# --------------------------------------------------------------------------- +# Authentication state (in-memory) +# --------------------------------------------------------------------------- + +# api_key -> device_id +_api_keys: dict[str, str] = {} + +# Rate limiting: api_key -> list of submission timestamps +_RATE_LIMIT_MAX = 10 # max submissions per window +_RATE_LIMIT_WINDOW = 3600 # 1 hour in seconds +_submission_timestamps: dict[str, list[float]] = defaultdict(list) + + +# --------------------------------------------------------------------------- +# Auth dependency +# --------------------------------------------------------------------------- + + +async def verify_api_key(x_api_key: str = Header(...)) -> tuple[str, str]: + """Validate the X-API-Key header. + + Returns a ``(api_key, device_id)`` tuple so that downstream handlers + can use the key for rate-limit tracking without a reverse lookup. + + Raises 401 if the key is not recognised. + """ + if x_api_key not in _api_keys: + raise HTTPException(status_code=401, detail="Invalid API key") + return x_api_key, _api_keys[x_api_key] + + +def _check_rate_limit(api_key: str) -> None: + """Enforce per-key submission rate limit. + + Raises 429 if the device has exceeded the maximum number of + submissions within the rolling time window. + """ + now = time.time() + cutoff = now - _RATE_LIMIT_WINDOW + + # Prune old timestamps + _submission_timestamps[api_key] = [ + ts for ts in _submission_timestamps[api_key] if ts > cutoff + ] + + if len(_submission_timestamps[api_key]) >= _RATE_LIMIT_MAX: + raise HTTPException( + status_code=429, + detail=( + f"Rate limit exceeded: max {_RATE_LIMIT_MAX} submissions " + f"per {_RATE_LIMIT_WINDOW // 60} minutes" + ), + ) + + _submission_timestamps[api_key].append(now) + + +# --------------------------------------------------------------------------- +# Public endpoints (no auth required) +# --------------------------------------------------------------------------- + + +@app.get("/health") +async def health(): + """Health check endpoint.""" + return {"status": "ok", "service": "sentineledge-hub"} + + +@app.get("/v1/model/version", response_model=ModelVersionInfo) +async def get_model_version(): + """Check current model version -- edge devices poll this to decide + whether to download a newer model. + """ + version, _ = model_store.get_latest() + if version == 0: + raise HTTPException(status_code=404, detail="No model available yet") + + meta = model_store.get_metadata(version) + if meta is None: + raise HTTPException(status_code=404, detail="Model metadata not found") + + return ModelVersionInfo( + model_version=version, + created_at=meta.get("created_at", ""), + n_contributing_devices=meta.get("n_contributing_devices", 0), + round_accuracy=meta.get("accuracy", 0.0), + ) + + +@app.get("/v1/round/status", response_model=RoundStatus) +async def get_round_status(): + """Get current federated round status.""" + return round_manager.get_status() + + +@app.get("/v1/metrics/global", response_model=GlobalMetrics) +async def get_global_metrics(): + """Aggregated stats only: total rounds, accuracy trend, etc. + + No per-device data is exposed -- privacy by design. + """ + metrics = round_manager.get_global_metrics() + return GlobalMetrics(**metrics) + + +@app.get("/v1/model/public_key") +async def get_public_key(): + """Return the hub's Ed25519 public key (hex) for signature verification. + + Edge devices use this to verify that model updates are authentic. + """ + return {"public_key": model_store.get_public_key()} + + +@app.post( + "/v1/devices/register", + response_model=DeviceRegistrationResponse, +) +async def register_device(registration: DeviceRegistration): + """Register a new edge device and receive an API key. + + The API key must be included in the ``X-API-Key`` header for all + authenticated endpoints (federated submit, model download). + """ + api_key = str(uuid.uuid4()) + _api_keys[api_key] = registration.device_id + logger.info( + "Device registered: device_id=%s (api_key=%s...)", + registration.device_id, + api_key[:8], + ) + return DeviceRegistrationResponse( + api_key=api_key, + device_id=registration.device_id, + ) + + +# --------------------------------------------------------------------------- +# Authenticated endpoints +# --------------------------------------------------------------------------- + + +@app.post("/v1/federated/submit", response_model=RoundStatus) +async def submit_update( + update: FederatedUpdate, + auth: tuple[str, str] = Depends(verify_api_key), +): + """Edge device submits a DP-noised gradient delta. + + Requires a valid ``X-API-Key`` header. + + The hub collects updates until `min_devices` are reached, then + automatically triggers federated averaging, validation, and + (if accepted) publishes a new global model version. + """ + api_key, device_id = auth + _check_rate_limit(api_key) + + try: + status = await round_manager.submit_update(update) + return status + except ValueError as exc: + raise HTTPException(status_code=400, detail=str(exc)) + except Exception as exc: + logger.error("Error processing update: %s", exc, exc_info=True) + raise HTTPException(status_code=500, detail="Internal aggregation error") + + +@app.get("/v1/model/latest", response_model=AggregationResponse) +async def get_latest_model( + auth: tuple[str, str] = Depends(verify_api_key), +): + """Edge device downloads the latest global model as a delta patch. + + Requires a valid ``X-API-Key`` header. + + Returns a base64-encoded gzip-compressed weight delta, along with + the Ed25519 signature so the device can verify authenticity. + """ + version, weights = model_store.get_latest() + if version == 0: + raise HTTPException(status_code=404, detail="No model available yet") + + try: + # Create delta from version 0 (full weights) -- edge devices + # that already have a previous version can request a specific delta + # via /v1/model/delta/{old}/{new} (future endpoint). + model_delta_b64 = model_store.create_delta_patch_b64( + old_version=0, new_version=version + ) + signature = model_store.get_signature_for_version(version) + meta = model_store.get_metadata(version) + + return AggregationResponse( + model_version=version, + model_delta=model_delta_b64, + signature=signature, + n_contributing_devices=meta.get("n_contributing_devices", 0) if meta else 0, + round_accuracy=meta.get("accuracy", 0.0) if meta else 0.0, + ) + except Exception as exc: + logger.error("Error creating model response: %s", exc, exc_info=True) + raise HTTPException(status_code=500, detail="Failed to prepare model") + + +# --------------------------------------------------------------------------- +# Entrypoint +# --------------------------------------------------------------------------- + +if __name__ == "__main__": + uvicorn.run(app, host="0.0.0.0", port=8080) diff --git a/hub/validator.py b/hub/validator.py new file mode 100644 index 0000000000000000000000000000000000000000..500359a55c4dee0a86944a95ca91618955e39669 --- /dev/null +++ b/hub/validator.py @@ -0,0 +1,289 @@ +"""Model validator for SentinelEdge Hub. + +Validates new global model weights against a held-out validation set +derived from public FTC fraud report data. Implements Byzantine fault +tolerance by rejecting model updates that degrade F1 by more than 2%. +""" + +import logging +import os + +import numpy as np + +logger = logging.getLogger(__name__) + +# Number of features the model expects (phone call fraud features). +DEFAULT_N_FEATURES = 25 + + +def _sigmoid(x: np.ndarray) -> np.ndarray: + """Numerically stable sigmoid.""" + return np.where( + x >= 0, + 1.0 / (1.0 + np.exp(-x)), + np.exp(x) / (1.0 + np.exp(x)), + ) + + +def _generate_synthetic_validation_data( + n_samples: int = 2000, + n_features: int = DEFAULT_N_FEATURES, + fraud_ratio: float = 0.15, + seed: int = 42, +) -> tuple[np.ndarray, np.ndarray]: + """Generate a synthetic validation set mimicking FTC fraud report patterns. + + This produces a reproducible dataset with realistic class imbalance + (~15% fraud) and feature distributions inspired by phone call fraud + indicators: call duration, time of day, caller ID mismatch, etc. + + Args: + n_samples: Total number of validation samples. + n_features: Number of features per sample. + fraud_ratio: Fraction of positive (fraud) samples. + seed: Random seed for reproducibility. + + Returns: + Tuple of (X, y) where X is (n_samples, n_features) and y is (n_samples,). + """ + rng = np.random.RandomState(seed) + + n_fraud = int(n_samples * fraud_ratio) + n_legit = n_samples - n_fraud + + # Legitimate calls: features drawn from a "normal" distribution + X_legit = rng.randn(n_legit, n_features) * 0.8 + + # Fraud calls: shifted distribution -- certain features are elevated + X_fraud = rng.randn(n_fraud, n_features) * 1.0 + # Fraud indicators: features 0-4 tend to be elevated in fraud calls + X_fraud[:, 0] += 2.0 # call duration anomaly + X_fraud[:, 1] += 1.5 # time-of-day anomaly + X_fraud[:, 2] += 1.8 # caller-id mismatch score + X_fraud[:, 3] += 1.2 # rapid callback pattern + X_fraud[:, 4] += 1.0 # geographic anomaly + + X = np.vstack([X_legit, X_fraud]) + y = np.concatenate([np.zeros(n_legit), np.ones(n_fraud)]) + + # Shuffle + perm = rng.permutation(n_samples) + X = X[perm] + y = y[perm] + + return X, y + + +class ModelValidator: + """Validates global model against held-out validation data. + + If a validation_data_path is provided and exists, loads it as a + numpy .npz file with keys 'X' and 'y'. Otherwise, generates a + reproducible synthetic dataset. + """ + + def __init__( + self, + validation_data_path: str = "validation_data.npz", + n_features: int = DEFAULT_N_FEATURES, + ): + """Load or generate validation dataset. + + Args: + validation_data_path: Path to .npz file with 'X' and 'y' keys. + n_features: Expected number of features (used for synthetic data). + """ + self.n_features = n_features + self.X: np.ndarray + self.y: np.ndarray + + if os.path.exists(validation_data_path): + try: + data = np.load(validation_data_path) + self.X = data["X"] + self.y = data["y"] + logger.info( + "Loaded validation data from %s: %d samples", + validation_data_path, + len(self.y), + ) + except Exception as e: + logger.warning( + "Failed to load %s (%s), generating synthetic data", + validation_data_path, + e, + ) + self.X, self.y = _generate_synthetic_validation_data( + n_features=n_features + ) + else: + logger.info( + "Validation data file not found at %s, " + "generating synthetic validation set", + validation_data_path, + ) + self.X, self.y = _generate_synthetic_validation_data( + n_features=n_features + ) + + logger.info( + "Validation set: %d samples, %.1f%% fraud", + len(self.y), + 100.0 * np.mean(self.y), + ) + + def _predict(self, model_weights: np.ndarray, threshold: float = 0.5) -> np.ndarray: + """Run logistic regression prediction with the given weights. + + The model is a simple linear model: y_hat = sigmoid(X @ w). + Weights vector is expected to be of length n_features (no bias) + or n_features+1 (last element is bias). + + Args: + model_weights: Weight vector. + threshold: Classification threshold. + + Returns: + Binary predictions array. + """ + w = np.array(model_weights, dtype=np.float64) + + if len(w) == self.X.shape[1] + 1: + # Last element is bias + logits = self.X @ w[:-1] + w[-1] + elif len(w) == self.X.shape[1]: + logits = self.X @ w + else: + # Dimension mismatch: truncate or pad with zeros + logger.warning( + "Weight dimension %d != feature dimension %d, adjusting", + len(w), + self.X.shape[1], + ) + adjusted = np.zeros(self.X.shape[1], dtype=np.float64) + n = min(len(w), self.X.shape[1]) + adjusted[:n] = w[:n] + logits = self.X @ adjusted + + probs = _sigmoid(logits) + return (probs >= threshold).astype(np.float64) + + def validate( + self, model_weights: np.ndarray, previous_f1: float + ) -> tuple[float, bool]: + """Test model, return (f1_score, should_accept). + + Reject if F1 drops more than 2% from previous round + (Byzantine fault tolerance). + + Args: + model_weights: New global model weight vector. + previous_f1: F1 score from the previous round. + + Returns: + Tuple of (f1_score, should_accept). + """ + metrics = self.compute_metrics(model_weights) + f1 = metrics["f1"] + + # Accept if F1 is within 2% of previous, or improved + max_allowed_drop = 0.02 + should_accept = f1 >= (previous_f1 - max_allowed_drop) + + if not should_accept: + logger.warning( + "Model REJECTED: F1=%.4f, previous=%.4f, drop=%.4f > %.4f", + f1, + previous_f1, + previous_f1 - f1, + max_allowed_drop, + ) + else: + logger.info( + "Model ACCEPTED: F1=%.4f (previous=%.4f, delta=%+.4f)", + f1, + previous_f1, + f1 - previous_f1, + ) + + return f1, should_accept + + def compute_metrics(self, model_weights: np.ndarray) -> dict: + """Compute full classification metrics. + + Args: + model_weights: Model weight vector. + + Returns: + Dict with keys: accuracy, precision, recall, f1, auc. + """ + y_pred = self._predict(model_weights) + y_true = self.y + + tp = float(np.sum((y_pred == 1) & (y_true == 1))) + fp = float(np.sum((y_pred == 1) & (y_true == 0))) + fn = float(np.sum((y_pred == 0) & (y_true == 1))) + tn = float(np.sum((y_pred == 0) & (y_true == 0))) + + accuracy = (tp + tn) / max(tp + tn + fp + fn, 1) + precision = tp / max(tp + fp, 1e-10) + recall = tp / max(tp + fn, 1e-10) + f1 = ( + 2 * precision * recall / max(precision + recall, 1e-10) + ) + + # Approximate AUC using the trapezoidal rule across thresholds + auc = self._compute_auc(model_weights) + + return { + "accuracy": round(accuracy, 4), + "precision": round(precision, 4), + "recall": round(recall, 4), + "f1": round(f1, 4), + "auc": round(auc, 4), + } + + def _compute_auc(self, model_weights: np.ndarray) -> float: + """Compute approximate AUC-ROC. + + Uses 100 threshold values to build the ROC curve and computes + area under curve via the trapezoidal rule. + """ + w = np.array(model_weights, dtype=np.float64) + + if len(w) == self.X.shape[1] + 1: + logits = self.X @ w[:-1] + w[-1] + elif len(w) == self.X.shape[1]: + logits = self.X @ w + else: + adjusted = np.zeros(self.X.shape[1], dtype=np.float64) + n = min(len(w), self.X.shape[1]) + adjusted[:n] = w[:n] + logits = self.X @ adjusted + + probs = _sigmoid(logits) + y_true = self.y + + thresholds = np.linspace(0, 1, 101) + tpr_list = [] + fpr_list = [] + + total_pos = max(np.sum(y_true == 1), 1) + total_neg = max(np.sum(y_true == 0), 1) + + for t in thresholds: + y_pred = (probs >= t).astype(np.float64) + tp = np.sum((y_pred == 1) & (y_true == 1)) + fp = np.sum((y_pred == 1) & (y_true == 0)) + tpr_list.append(tp / total_pos) + fpr_list.append(fp / total_neg) + + # Sort by FPR for proper AUC calculation + fpr_arr = np.array(fpr_list) + tpr_arr = np.array(tpr_list) + sorted_idx = np.argsort(fpr_arr) + fpr_sorted = fpr_arr[sorted_idx] + tpr_sorted = tpr_arr[sorted_idx] + + auc = float(np.trapezoid(tpr_sorted, fpr_sorted)) + return max(0.0, min(1.0, abs(auc))) diff --git a/models/call_fraud_mlp.npz b/models/call_fraud_mlp.npz new file mode 100644 index 0000000000000000000000000000000000000000..e4bcea298f20bccbaadb186944cd4870d3cf221c --- /dev/null +++ b/models/call_fraud_mlp.npz @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:2c515fdc571749927a27adf390a258f07667169b5f979fc46d84cc945bc0898b +size 240146 diff --git a/models/call_fraud_xgb.json b/models/call_fraud_xgb.json new file mode 100644 index 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\ No newline at end of file diff --git a/requirements-deploy.txt b/requirements-deploy.txt new file mode 100644 index 0000000000000000000000000000000000000000..d090148bdd9f630455bc17af6b8a7d9e15d9d8aa --- /dev/null +++ b/requirements-deploy.txt @@ -0,0 +1,11 @@ +# Minimal deps for HF Spaces deployment (no whisper/pyaudio/onnx) +numpy>=1.24 +scikit-learn>=1.3 +xgboost>=2.0 +pandas>=2.1 +joblib>=1.3 +fastapi>=0.104 +uvicorn[standard]>=0.24 +websockets>=12.0 +pydantic>=2.5 +PyNaCl>=1.5 diff --git a/sentinel_edge/__init__.py b/sentinel_edge/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..a96ee77f0533aaa04ca97959b29684040f226a5b --- /dev/null +++ b/sentinel_edge/__init__.py @@ -0,0 +1,3 @@ +"""SentinelEdge - Federated Edge AI for Real-Time Phone Call Fraud Detection.""" + +__version__ = "0.1.0" diff --git a/sentinel_edge/audio/__init__.py b/sentinel_edge/audio/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..d244667021882230b44cad04d740cc5d98791a2c --- /dev/null +++ b/sentinel_edge/audio/__init__.py @@ -0,0 +1 @@ +"""Audio pipeline: capture, buffer, transcription.""" diff --git a/sentinel_edge/audio/ring_buffer.py b/sentinel_edge/audio/ring_buffer.py new file mode 100644 index 0000000000000000000000000000000000000000..09379788761a41fffdc9e767086507c30703a390 --- /dev/null +++ b/sentinel_edge/audio/ring_buffer.py @@ -0,0 +1,131 @@ +"""Circular (ring) buffer for raw PCM audio samples. + +Provides O(1) writes and efficient reads of the most recent *N* seconds +of audio, designed for real-time phone call capture on memory-constrained +edge devices. A 30-second buffer at 16 kHz / 16-bit mono consumes +only ~960 KB. +""" + +from __future__ import annotations + +import numpy as np + + +class RingBuffer: + """Fixed-size circular buffer backed by a NumPy int16 array. + + Parameters + ---------- + duration_seconds : float + Maximum buffer duration in seconds. + sample_rate : int + Audio sample rate in Hz (default 16 000). + """ + + def __init__( + self, + duration_seconds: float = 30.0, + sample_rate: int = 16_000, + ) -> None: + self.sample_rate = sample_rate + self.capacity = int(duration_seconds * sample_rate) + self.buffer = np.zeros(self.capacity, dtype=np.int16) + self.write_pos: int = 0 + self.total_written: int = 0 # cumulative sample count + + # ------------------------------------------------------------------ + # Write + # ------------------------------------------------------------------ + + def write(self, samples: np.ndarray) -> None: + """Append PCM samples to the buffer, wrapping as needed. + + Parameters + ---------- + samples : np.ndarray + 1-D array of int16 PCM samples. May be longer than the + buffer capacity -- only the last ``capacity`` samples are + retained in that case. + """ + samples = np.asarray(samples, dtype=np.int16).ravel() + n = len(samples) + + if n == 0: + return + + # If the incoming chunk is larger than the buffer, keep only the + # tail that fits. + if n >= self.capacity: + samples = samples[-self.capacity:] + n = self.capacity + self.buffer[:] = samples + self.write_pos = 0 + self.total_written += n + return + + # Number of samples that fit before wrapping + first_chunk = min(n, self.capacity - self.write_pos) + self.buffer[self.write_pos : self.write_pos + first_chunk] = samples[:first_chunk] + + # Wrap-around portion (if any) + remaining = n - first_chunk + if remaining > 0: + self.buffer[:remaining] = samples[first_chunk:] + + self.write_pos = (self.write_pos + n) % self.capacity + self.total_written += n + + # ------------------------------------------------------------------ + # Read + # ------------------------------------------------------------------ + + def read_last(self, duration_seconds: float) -> np.ndarray: + """Read the most recent *duration_seconds* of audio. + + Parameters + ---------- + duration_seconds : float + How many seconds of audio to retrieve. + + Returns + ------- + np.ndarray + 1-D int16 array. May be shorter than requested if the + buffer has not yet been fully written. + """ + n_requested = int(duration_seconds * self.sample_rate) + n_available = min(n_requested, self.capacity, self.total_written) + + if n_available == 0: + return np.array([], dtype=np.int16) + + start = (self.write_pos - n_available) % self.capacity + + if start < self.write_pos: + return self.buffer[start : self.write_pos].copy() + else: + # Data wraps around the end of the buffer + return np.concatenate([ + self.buffer[start:], + self.buffer[: self.write_pos], + ]) + + # ------------------------------------------------------------------ + # Utilities + # ------------------------------------------------------------------ + + def clear(self) -> None: + """Zero-fill the buffer and reset write position.""" + self.buffer[:] = 0 + self.write_pos = 0 + self.total_written = 0 + + @property + def duration_available(self) -> float: + """Seconds of valid audio currently in the buffer.""" + return min(self.total_written, self.capacity) / self.sample_rate + + @property + def is_full(self) -> bool: + """Whether the buffer has been completely written at least once.""" + return self.total_written >= self.capacity diff --git a/sentinel_edge/audio/sentence_splitter.py b/sentinel_edge/audio/sentence_splitter.py new file mode 100644 index 0000000000000000000000000000000000000000..72f2eb942d5823ec4b9f412e2dce5d91784d814c --- /dev/null +++ b/sentinel_edge/audio/sentence_splitter.py @@ -0,0 +1,120 @@ +"""Rule-based sentence segmentation for streaming transcripts. + +Designed for incremental use: text is fed in as it arrives from the +transcriber and complete sentences are emitted as soon as a sentence +boundary is detected. +""" + +from __future__ import annotations + +import re + + +# Sentence-ending patterns: +# - Standard punctuation: . ! ? +# - Ellipsis / long pause: ... (treated as a sentence boundary) +# We look for a sentence terminator followed by whitespace or end-of-string +# to avoid splitting on abbreviations like "U.S.A." mid-sentence. +_SENTENCE_BOUNDARY = re.compile( + r"(?<=[.!?])" # lookbehind for sentence-ending punctuation + r"(?:\s+|$)" # followed by whitespace or end-of-string + r"|" + r"(?<=\.\.\.)" # lookbehind for ellipsis + r"(?:\s+|$)" # followed by whitespace or end-of-string +) + + +class SentenceSplitter: + """Incrementally segments streaming transcript text into sentences. + + Usage:: + + splitter = SentenceSplitter() + for chunk in transcript_stream: + new_sentences = splitter.feed(chunk) + for sentence in new_sentences: + process(sentence) + # At end-of-stream, flush any remaining text + leftover = splitter.flush() + if leftover: + process(leftover) + """ + + def __init__(self) -> None: + self.buffer: str = "" + self.sentences: list[str] = [] + + # ------------------------------------------------------------------ + # Public API + # ------------------------------------------------------------------ + + def feed(self, text: str) -> list[str]: + """Append *text* to the internal buffer and extract complete sentences. + + Parameters + ---------- + text : str + New transcript text (may be a partial sentence, a full + sentence, or multiple sentences). + + Returns + ------- + list[str] + Newly completed sentences (may be empty if no boundary was + found yet). + """ + self.buffer += text + completed: list[str] = [] + + # Repeatedly split at the first sentence boundary + while True: + match = _SENTENCE_BOUNDARY.search(self.buffer) + if match is None: + break + + boundary_end = match.end() + sentence = self.buffer[:boundary_end].strip() + + if sentence: + completed.append(sentence) + self.sentences.append(sentence) + + self.buffer = self.buffer[boundary_end:] + + return completed + + def flush(self) -> str | None: + """Return any remaining buffered text as the final sentence. + + Returns + ------- + str | None + The leftover text, or *None* if the buffer is empty. + """ + remaining = self.buffer.strip() + if remaining: + self.sentences.append(remaining) + self.buffer = "" + return remaining + + self.buffer = "" + return None + + def reset(self) -> None: + """Clear all internal state for a new transcript.""" + self.buffer = "" + self.sentences.clear() + + # ------------------------------------------------------------------ + # Properties + # ------------------------------------------------------------------ + + @property + def all_sentences(self) -> list[str]: + """All sentences extracted so far (including flushed).""" + return list(self.sentences) + + @property + def pending(self) -> str: + """Text currently waiting in the buffer (not yet a full sentence).""" + return self.buffer diff --git a/sentinel_edge/audio/transcriber.py b/sentinel_edge/audio/transcriber.py new file mode 100644 index 0000000000000000000000000000000000000000..fe85f5a7be4458a603404020741b4a4f5101e42c --- /dev/null +++ b/sentinel_edge/audio/transcriber.py @@ -0,0 +1,137 @@ +"""Whisper-based audio transcription wrapper. + +For the demo / development environment the standard ``openai-whisper`` +Python package is used. In a production edge deployment this would be +swapped for an ONNX Runtime Mobile session running a quantised Whisper +encoder/decoder -- the public interface stays identical. +""" + +from __future__ import annotations + +import logging +from pathlib import Path + +import numpy as np + +logger = logging.getLogger(__name__) + + +class Transcriber: + """Lazy-loading wrapper around OpenAI Whisper. + + The model is loaded on the first call to :meth:`load` (or + transparently on the first transcription request) to keep cold-start + latency out of import time. + + Parameters + ---------- + model_name : str + Whisper model size. ``"tiny.en"`` is recommended for on-device + real-time use (< 75 MB, ~10x realtime on CPU). + """ + + def __init__(self, model_name: str = "tiny.en") -> None: + self.model = None + self.model_name = model_name + self._loaded = False + + # ------------------------------------------------------------------ + # Model lifecycle + # ------------------------------------------------------------------ + + def load(self) -> None: + """Eagerly load the Whisper model into memory. + + This is separated from ``__init__`` so callers can control *when* + the ~75 MB model is fetched and loaded. + """ + import whisper # type: ignore[import-untyped] + + logger.info("Loading Whisper model '%s' ...", self.model_name) + self.model = whisper.load_model(self.model_name) + self._loaded = True + logger.info("Whisper model loaded.") + + def _ensure_loaded(self) -> None: + """Load the model lazily if it has not been loaded yet.""" + if not self._loaded: + self.load() + + # ------------------------------------------------------------------ + # Transcription + # ------------------------------------------------------------------ + + def transcribe( + self, + audio: np.ndarray, + sample_rate: int = 16_000, + ) -> str: + """Transcribe a raw audio array to text. + + Parameters + ---------- + audio : np.ndarray + 1-D float32 array normalised to ``[-1.0, 1.0]``. + If int16 is passed it will be converted automatically. + sample_rate : int + Sample rate of the input audio. If not 16 000 Hz the audio + is resampled internally by Whisper. + + Returns + ------- + str + Transcribed text (stripped of leading/trailing whitespace). + """ + self._ensure_loaded() + + audio = np.asarray(audio) + + # Convert int16 PCM to float32 normalised + if audio.dtype == np.int16: + audio = audio.astype(np.float32) / 32768.0 + elif audio.dtype != np.float32: + audio = audio.astype(np.float32) + + # Whisper expects float32, mono, 16 kHz + # The library handles resampling internally if sr != 16000 + result = self.model.transcribe( + audio, + language="en", + fp16=False, # safe for CPU-only environments + ) + return result.get("text", "").strip() + + def transcribe_file(self, wav_path: str) -> str: + """Transcribe a WAV file from disk. + + Parameters + ---------- + wav_path : str + Path to a WAV (or other ffmpeg-supported) audio file. + + Returns + ------- + str + Transcribed text. + """ + self._ensure_loaded() + + path = Path(wav_path) + if not path.exists(): + raise FileNotFoundError(f"Audio file not found: {wav_path}") + + result = self.model.transcribe( + str(path), + language="en", + fp16=False, + ) + return result.get("text", "").strip() + + # ------------------------------------------------------------------ + # Properties + # ------------------------------------------------------------------ + + @property + def is_loaded(self) -> bool: + """Whether the Whisper model is currently in memory.""" + return self._loaded diff --git a/sentinel_edge/audio/windowing.py b/sentinel_edge/audio/windowing.py new file mode 100644 index 0000000000000000000000000000000000000000..b8b4244323b14fc8c4803fd6c78cf248fa639a22 --- /dev/null +++ b/sentinel_edge/audio/windowing.py @@ -0,0 +1,192 @@ +"""Sliding window extraction and mel-spectrogram computation. + +Implements a lightweight mel-spectrogram using only NumPy and SciPy +(no librosa dependency), suitable for edge deployment where every +megabyte of installed packages matters. +""" + +from __future__ import annotations + +import numpy as np +from scipy.fft import rfft, rfftfreq # type: ignore[import-untyped] +from scipy.signal import get_window # type: ignore[import-untyped] + +from sentinel_edge.audio.ring_buffer import RingBuffer + + +def _hz_to_mel(hz: float) -> float: + """Convert frequency in Hz to the mel scale.""" + return 2595.0 * np.log10(1.0 + hz / 700.0) + + +def _mel_to_hz(mel: float) -> float: + """Convert mel-scale value back to Hz.""" + return 700.0 * (10.0 ** (mel / 2595.0) - 1.0) + + +def _mel_filterbank( + n_mels: int, + n_fft: int, + sample_rate: int, + fmin: float = 0.0, + fmax: float | None = None, +) -> np.ndarray: + """Build a mel-scaled triangular filterbank matrix. + + Parameters + ---------- + n_mels : int + Number of mel bands. + n_fft : int + FFT size (determines frequency resolution). + sample_rate : int + Audio sample rate in Hz. + fmin, fmax : float + Minimum / maximum frequency for the filterbank. + + Returns + ------- + np.ndarray + Shape ``(n_mels, n_fft // 2 + 1)`` filterbank matrix. + """ + if fmax is None: + fmax = sample_rate / 2.0 + + # Mel-spaced centre frequencies + mel_min = _hz_to_mel(fmin) + mel_max = _hz_to_mel(fmax) + mel_points = np.linspace(mel_min, mel_max, n_mels + 2) + hz_points = np.array([_mel_to_hz(m) for m in mel_points]) + + # Map Hz points to FFT bin indices + n_freqs = n_fft // 2 + 1 + bin_points = np.floor((n_fft + 1) * hz_points / sample_rate).astype(int) + + filterbank = np.zeros((n_mels, n_freqs), dtype=np.float64) + for i in range(n_mels): + left = bin_points[i] + centre = bin_points[i + 1] + right = bin_points[i + 2] + + # Rising slope + for j in range(left, centre): + if centre != left: + filterbank[i, j] = (j - left) / (centre - left) + + # Falling slope + for j in range(centre, right): + if right != centre: + filterbank[i, j] = (right - j) / (right - centre) + + return filterbank + + +class AudioWindower: + """Extracts fixed-length audio windows and converts them to mel spectrograms. + + Parameters + ---------- + window_size : float + Duration of each analysis window in seconds. + hop_size : float + Step between consecutive windows in seconds (controls overlap). + sample_rate : int + Audio sample rate in Hz. + n_mels : int + Number of mel frequency bands. + n_fft : int + FFT window size in samples. Defaults to 512 (~32 ms at 16 kHz). + hop_length : int + FFT hop length in samples. Defaults to 160 (~10 ms at 16 kHz). + """ + + def __init__( + self, + window_size: float = 5.0, + hop_size: float = 1.0, + sample_rate: int = 16_000, + n_mels: int = 80, + n_fft: int = 512, + hop_length: int = 160, + ) -> None: + self.window_size = window_size + self.hop_size = hop_size + self.sample_rate = sample_rate + self.n_mels = n_mels + self.n_fft = n_fft + self.hop_length = hop_length + + # Pre-compute the mel filterbank and STFT window + self._filterbank = _mel_filterbank( + n_mels=n_mels, + n_fft=n_fft, + sample_rate=sample_rate, + ) + self._fft_window = get_window("hann", n_fft, fftbins=True) + + # ------------------------------------------------------------------ + # Public API + # ------------------------------------------------------------------ + + def extract_window(self, ring_buffer: RingBuffer) -> np.ndarray | None: + """Grab the latest window from the ring buffer and compute its mel spectrogram. + + Parameters + ---------- + ring_buffer : RingBuffer + Active audio ring buffer. + + Returns + ------- + np.ndarray | None + Log-mel spectrogram of shape ``(n_mels, T)`` or *None* if the + ring buffer does not yet contain enough data. + """ + if ring_buffer.duration_available < self.window_size: + return None + + samples = ring_buffer.read_last(self.window_size) + audio_float = samples.astype(np.float32) / 32768.0 + return self.compute_mel_spectrogram(audio_float) + + def compute_mel_spectrogram(self, audio: np.ndarray) -> np.ndarray: + """Compute a log-mel spectrogram from a float32 audio waveform. + + Parameters + ---------- + audio : np.ndarray + 1-D float32 array normalised to ``[-1, 1]``. + + Returns + ------- + np.ndarray + Log-mel spectrogram of shape ``(n_mels, T)`` where ``T`` is + the number of STFT frames. + """ + audio = np.asarray(audio, dtype=np.float64) + + # --- Short-Time Fourier Transform (STFT) --- + n_samples = len(audio) + n_frames = 1 + (n_samples - self.n_fft) // self.hop_length + if n_frames <= 0: + # Audio too short for even one FFT frame + return np.zeros((self.n_mels, 1), dtype=np.float64) + + # Build the spectrogram frame-by-frame + stft_matrix = np.zeros( + (self.n_fft // 2 + 1, n_frames), dtype=np.float64 + ) + for t in range(n_frames): + start = t * self.hop_length + frame = audio[start : start + self.n_fft] * self._fft_window + spectrum = rfft(frame) + stft_matrix[:, t] = np.abs(spectrum) ** 2 # power spectrum + + # --- Mel filterbank application --- + mel_spec = self._filterbank @ stft_matrix # (n_mels, T) + + # --- Log compression --- + # Add a small floor to avoid log(0) + log_mel = np.log(np.maximum(mel_spec, 1e-10)) + + return log_mel diff --git a/sentinel_edge/classifier/__init__.py b/sentinel_edge/classifier/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..81e1c9ec5be21be2e2aead62e19580292fcfb522 --- /dev/null +++ b/sentinel_edge/classifier/__init__.py @@ -0,0 +1 @@ +"""Fraud classification models.""" diff --git a/sentinel_edge/classifier/alert_engine.py b/sentinel_edge/classifier/alert_engine.py new file mode 100644 index 0000000000000000000000000000000000000000..e99a9340cd5dd478b2eed2fab6d66d2854c2c9ea --- /dev/null +++ b/sentinel_edge/classifier/alert_engine.py @@ -0,0 +1,202 @@ +"""Alert engine -- threshold logic and human-readable reason generation. + +Translates a smoothed EMA fraud score and the underlying handcrafted +features into an ``AlertDecision`` that the UI layer can act on. +""" + +from __future__ import annotations + +from dataclasses import dataclass, field +from enum import Enum +from typing import Any + + +class RiskLevel(str, Enum): + """Discrete risk tiers displayed to the end-user.""" + + SAFE = "safe" + LOW = "low" + MEDIUM = "medium" + HIGH = "high" + CRITICAL = "critical" + + +@dataclass +class AlertDecision: + """Immutable decision produced by :class:`AlertEngine.evaluate`. + + Attributes + ---------- + should_alert : bool + Whether the UI should display an active warning overlay. + risk_level : RiskLevel + Categorical risk tier. + score : float + The smoothed EMA score that drove the decision. + reasons : list[str] + Human-readable explanations derived from the handcrafted features + (e.g. "Contains IRS impersonation language"). + """ + + should_alert: bool + risk_level: RiskLevel + score: float + reasons: list[str] = field(default_factory=list) + + +class AlertEngine: + """Stateless evaluator that maps scores + features to alert decisions. + + Parameters + ---------- + red_threshold : float + EMA score at or above which a CRITICAL alert is issued and the + full red overlay is shown. + amber_threshold : float + EMA score at or above which a HIGH (amber) caution is issued. + """ + + def __init__( + self, + red_threshold: float = 0.75, + amber_threshold: float = 0.5, + ) -> None: + if not (0.0 < amber_threshold < red_threshold <= 1.0): + raise ValueError( + "Thresholds must satisfy 0 < amber < red <= 1.0, " + f"got amber={amber_threshold}, red={red_threshold}" + ) + self.red_threshold = red_threshold + self.amber_threshold = amber_threshold + + # ------------------------------------------------------------------ + # Public API + # ------------------------------------------------------------------ + + def evaluate( + self, + ema_score: float, + features: dict[str, float], + ) -> AlertDecision: + """Produce an alert decision from the current EMA score and features. + + Parameters + ---------- + ema_score : float + Smoothed fraud score from the ``ScoreAccumulator``. + features : dict[str, float] + Handcrafted feature dict (18 keys) from ``extract_handcrafted_features``. + + Returns + ------- + AlertDecision + """ + risk_level = self._classify_risk(ema_score) + reasons = self._generate_reasons(features) + should_alert = risk_level in (RiskLevel.CRITICAL, RiskLevel.HIGH) + + return AlertDecision( + should_alert=should_alert, + risk_level=risk_level, + score=ema_score, + reasons=reasons, + ) + + # ------------------------------------------------------------------ + # Internals + # ------------------------------------------------------------------ + + def _classify_risk(self, ema_score: float) -> RiskLevel: + """Map a continuous score to a discrete risk tier.""" + if ema_score >= self.red_threshold: + return RiskLevel.CRITICAL + if ema_score >= self.amber_threshold: + return RiskLevel.HIGH + if ema_score >= 0.3: + return RiskLevel.MEDIUM + if ema_score >= 0.15: + return RiskLevel.LOW + return RiskLevel.SAFE + + def _generate_reasons(self, features: dict[str, float]) -> list[str]: + """Derive human-readable explanations from the handcrafted features. + + Parameters + ---------- + features : dict[str, float] + Output of ``extract_handcrafted_features``. + + Returns + ------- + list[str] + Possibly empty list of explanation strings. + """ + reasons: list[str] = [] + + # --- Impersonation --- + imp = features.get("impersonation_count", 0.0) + if imp >= 1: + reasons.append( + "Contains government/corporate impersonation language" + ) + + # --- Urgency --- + urg = features.get("urgency_count", 0.0) + if urg >= 2: + reasons.append("Multiple urgency tactics detected") + elif urg == 1: + reasons.append("Urgency language detected") + + # --- Action pressure --- + act = features.get("action_count", 0.0) + if act >= 2: + reasons.append("Pressure to take immediate action") + elif act == 1: + reasons.append("Prompts user to perform a specific action") + + # --- Financial references --- + fin = features.get("financial_count", 0.0) + if fin >= 1: + reasons.append("References financial accounts or transactions") + + # --- Threats --- + if features.get("has_threat", 0.0): + reasons.append("Threatening language detected (arrest, suspension, etc.)") + + # --- Prize / lottery --- + if features.get("has_prize", 0.0): + reasons.append("Prize or lottery claim detected") + + # --- URLs --- + if features.get("has_url", 0.0): + reasons.append("Contains a URL -- possible phishing link") + if features.get("has_shortened_url", 0.0): + reasons.append("Contains a shortened URL hiding the true destination") + + # --- Account reference --- + if features.get("has_account_ref", 0.0): + reasons.append("Requests sensitive account or credential information") + + # --- Verify pattern --- + if features.get("has_verify_pattern", 0.0): + reasons.append("Asks to verify or confirm identity/account") + + # --- Dollar sign --- + if features.get("dollar_sign", 0.0): + reasons.append("References a monetary amount") + + # --- Phone number --- + if features.get("has_phone_number", 0.0): + reasons.append("Includes a phone number -- possible callback scam") + + # --- Excessive caps --- + caps = features.get("caps_ratio", 0.0) + if caps >= 0.5: + reasons.append("Excessive use of capital letters") + + # --- Exclamations --- + excl = features.get("exclamation_count", 0.0) + if excl >= 3: + reasons.append("Excessive exclamation marks suggest high-pressure tactics") + + return reasons diff --git a/sentinel_edge/classifier/mlp_classifier.py b/sentinel_edge/classifier/mlp_classifier.py new file mode 100644 index 0000000000000000000000000000000000000000..bf4bc1fd80e601686363a30b2c2eaaffb0c22822 --- /dev/null +++ b/sentinel_edge/classifier/mlp_classifier.py @@ -0,0 +1,280 @@ +"""MLP fraud classifier -- differentiable for federated learning. + +Architecture: input_dim -> 128 -> ReLU -> 64 -> ReLU -> 1 -> Sigmoid + +The classifier exposes ``get_weights`` / ``set_weights`` so that the +federated learning protocol can exchange flat weight vectors between +edge devices and the central hub. + +For input_dim=402 (18 handcrafted + 384 embedding): + Total params: 402*128 + 128 + 128*64 + 64 + 64*1 + 1 = 59,969 + Size: ~240 KB (float32) +""" + +from __future__ import annotations + +import logging +from pathlib import Path + +import numpy as np + +logger = logging.getLogger(__name__) + + +class MLPClassifier: + """Numpy-based MLP binary classifier with federated-learning support. + + Parameters + ---------- + input_dim : int + Number of input features. Default 402 matches the embedding + pipeline (18 handcrafted + 384 sentence embedding). + """ + + def __init__(self, input_dim: int = 402) -> None: + self.input_dim = input_dim + self._init_weights() + + # ------------------------------------------------------------------ + # Weight initialisation (He / Kaiming) + # ------------------------------------------------------------------ + + def _init_weights(self) -> None: + """Initialise weights using He initialisation with float64 for + numerical stability during federated training.""" + rng = np.random.default_rng(seed=42) + + # Use small-scale init (0.01) instead of He init to prevent + # dead ReLUs and numerical overflow in federated training where + # inputs are normalized z-scores (mean=0, std=1). + scale1 = 0.01 + scale2 = 0.01 + scale3 = 0.01 + + self.W1 = (rng.standard_normal((self.input_dim, 128)) * scale1).astype(np.float64) + self.b1 = np.zeros(128, dtype=np.float64) + + self.W2 = (rng.standard_normal((128, 64)) * scale2).astype(np.float64) + self.b2 = np.zeros(64, dtype=np.float64) + + self.W3 = (rng.standard_normal((64, 1)) * scale3).astype(np.float64) + self.b3 = np.zeros(1, dtype=np.float64) + + # ------------------------------------------------------------------ + # Forward pass + # ------------------------------------------------------------------ + + def forward(self, x: np.ndarray) -> float: + """Run the forward pass for a single sample. + + Parameters + ---------- + x : np.ndarray + 1-D feature vector of length ``input_dim``. + + Returns + ------- + float + Fraud probability in ``[0, 1]``. + """ + x = np.asarray(x, dtype=np.float64) + if x.ndim == 2: + # Batch input: return full array (not just first element) + return self.forward_batch(x) + h1 = np.maximum(0, x @ self.W1 + self.b1) # ReLU + h2 = np.maximum(0, h1 @ self.W2 + self.b2) # ReLU + logit_arr = h2 @ self.W3 + self.b3 # shape (1,) + logit = float(logit_arr.item()) # numpy 2.x safe + return 1.0 / (1.0 + np.exp(-np.clip(logit, -50, 50))) # Sigmoid + + def forward_batch(self, X: np.ndarray) -> np.ndarray: + """Run the forward pass for a batch of samples. + + Parameters + ---------- + X : np.ndarray + 2-D array of shape ``(n_samples, input_dim)``. + + Returns + ------- + np.ndarray + 1-D array of fraud probabilities, shape ``(n_samples,)``. + """ + X = np.asarray(X, dtype=np.float64) + z1 = X @ self.W1 + self.b1 + z1 = np.nan_to_num(z1, nan=0.0, posinf=50.0, neginf=-50.0) + h1 = np.maximum(0, z1) + z2 = h1 @ self.W2 + self.b2 + z2 = np.nan_to_num(z2, nan=0.0, posinf=50.0, neginf=-50.0) + h2 = np.maximum(0, z2) + logits = (h2 @ self.W3 + self.b3).ravel() + logits = np.nan_to_num(logits, nan=0.0, posinf=50.0, neginf=-50.0) + return 1.0 / (1.0 + np.exp(-np.clip(logits, -50, 50))) + + # ------------------------------------------------------------------ + # Prediction interface (drop-in compatible with FraudClassifier) + # ------------------------------------------------------------------ + + def predict_proba(self, features: np.ndarray) -> float: + """Return fraud probability for a single feature vector. + + Same interface as ``FraudClassifier.predict_proba`` for + drop-in replacement in the engine. + """ + return float(self.forward(features)) + + def predict( + self, features: np.ndarray, threshold: float = 0.5 + ) -> tuple[bool, float]: + """Binary prediction with confidence score. + + Parameters + ---------- + features : np.ndarray + 1-D feature vector. + threshold : float + Decision threshold. + + Returns + ------- + tuple[bool, float] + ``(is_fraud, confidence)``. + """ + prob = self.predict_proba(features) + return prob >= threshold, prob + + # ------------------------------------------------------------------ + # Federated learning: weight serialisation + # ------------------------------------------------------------------ + + def get_weights(self) -> np.ndarray: + """Flatten all weights into a single 1-D vector. + + Order: W1, b1, W2, b2, W3, b3. + + Returns + ------- + np.ndarray + 1-D float32 vector of length ``n_params``. + """ + return np.concatenate( + [ + self.W1.ravel(), + self.b1, + self.W2.ravel(), + self.b2, + self.W3.ravel(), + self.b3, + ] + ) + + def set_weights(self, weights: np.ndarray) -> None: + """Set all weights from a flat vector (inverse of ``get_weights``). + + Parameters + ---------- + weights : np.ndarray + 1-D vector of length ``n_params``. + """ + weights = np.asarray(weights, dtype=np.float64) + idx = 0 + + # W1: (input_dim, 128) + n = self.input_dim * 128 + self.W1 = weights[idx : idx + n].reshape(self.input_dim, 128) + idx += n + + # b1: (128,) + self.b1 = weights[idx : idx + 128].copy() + idx += 128 + + # W2: (128, 64) + n = 128 * 64 + self.W2 = weights[idx : idx + n].reshape(128, 64) + idx += n + + # b2: (64,) + self.b2 = weights[idx : idx + 64].copy() + idx += 64 + + # W3: (64, 1) + n = 64 + self.W3 = weights[idx : idx + n].reshape(64, 1) + idx += n + + # b3: (1,) + self.b3 = weights[idx : idx + 1].copy() + + @property + def n_params(self) -> int: + """Total number of trainable parameters.""" + return ( + self.input_dim * 128 + + 128 + + 128 * 64 + + 64 + + 64 * 1 + + 1 + ) + + # ------------------------------------------------------------------ + # Persistence + # ------------------------------------------------------------------ + + def save(self, path: str) -> None: + """Save model weights to a ``.npz`` file. + + Saves via the flat weight vector so that ``load`` can restore + using ``set_weights``, keeping serialisation consistent with + the federated learning protocol. + + Parameters + ---------- + path : str + Destination path (e.g. ``models/call_fraud_mlp.npz``). + """ + dest = Path(path) + dest.parent.mkdir(parents=True, exist_ok=True) + np.savez( + dest, + weights=self.get_weights(), + input_dim=np.array(self.input_dim), + ) + logger.info( + "Saved MLP classifier (%d params) to %s", self.n_params, path + ) + + @classmethod + def load(cls, path: str) -> MLPClassifier: + """Load model weights from a ``.npz`` file. + + Parameters + ---------- + path : str + Path to a previously saved ``.npz`` file. + + Returns + ------- + MLPClassifier + Restored classifier instance. + """ + data = np.load(path) + model = cls(input_dim=int(data["input_dim"])) + model.set_weights(data["weights"]) + logger.info( + "Loaded MLP classifier (%d params) from %s", + model.n_params, + path, + ) + return model + + # ------------------------------------------------------------------ + # Representation + # ------------------------------------------------------------------ + + def __repr__(self) -> str: + return ( + f"MLPClassifier(input_dim={self.input_dim}, " + f"arch=[{self.input_dim}->128->64->1], " + f"params={self.n_params:,})" + ) diff --git a/sentinel_edge/classifier/score_accumulator.py b/sentinel_edge/classifier/score_accumulator.py new file mode 100644 index 0000000000000000000000000000000000000000..53fd7c7dfed5b559f10f01d155510eb311b0bbc2 --- /dev/null +++ b/sentinel_edge/classifier/score_accumulator.py @@ -0,0 +1,80 @@ +"""Exponential Moving Average (EMA) score accumulator. + +Smooths per-sentence fraud scores over a phone call so that a single +high-scoring sentence doesn't cause a spurious alert while sustained +suspicious language steadily raises the risk level. +""" + +from __future__ import annotations + + +class ScoreAccumulator: + """Maintains an EMA of per-sentence fraud scores. + + Parameters + ---------- + alpha : float + Smoothing factor in ``(0, 1]``. Higher values weight recent + sentences more heavily. ``alpha = 1.0`` disables smoothing. + """ + + def __init__(self, alpha: float = 0.3) -> None: + if not (0.0 < alpha <= 1.0): + raise ValueError(f"alpha must be in (0, 1], got {alpha}") + self.alpha = alpha + self.ema: float = 0.0 + self.count: int = 0 + self.history: list[float] = [] + + # ------------------------------------------------------------------ + # Public API + # ------------------------------------------------------------------ + + def update(self, new_score: float) -> float: + """Incorporate a new sentence score and return the updated EMA. + + Parameters + ---------- + new_score : float + Raw fraud probability for the latest sentence (0.0 -- 1.0). + + Returns + ------- + float + Updated EMA value. + """ + self.history.append(new_score) + self.count += 1 + + if self.count == 1: + # Bootstrap: initialise EMA to the first observation + self.ema = new_score + else: + self.ema = self.alpha * new_score + (1.0 - self.alpha) * self.ema + + return self.ema + + def reset(self) -> None: + """Clear accumulated state for a new call.""" + self.ema = 0.0 + self.count = 0 + self.history.clear() + + # ------------------------------------------------------------------ + # Properties + # ------------------------------------------------------------------ + + @property + def current_score(self) -> float: + """The latest EMA value (0.0 if no data has been accumulated).""" + return self.ema + + @property + def peak_score(self) -> float: + """Highest individual sentence score seen so far.""" + return max(self.history) if self.history else 0.0 + + @property + def mean_score(self) -> float: + """Simple arithmetic mean of all sentence scores.""" + return sum(self.history) / len(self.history) if self.history else 0.0 diff --git a/sentinel_edge/classifier/xgb_classifier.py b/sentinel_edge/classifier/xgb_classifier.py new file mode 100644 index 0000000000000000000000000000000000000000..b9ce11f1de936167171029bef5588fea03cae50f --- /dev/null +++ b/sentinel_edge/classifier/xgb_classifier.py @@ -0,0 +1,163 @@ +"""XGBoost fraud classifier with native and ONNX inference support. + +Loads a trained binary classifier from either: + - A native XGBoost JSON checkpoint (``.json``) + - An ONNX model exported from XGBoost (``.onnx``) + +and exposes a simple ``predict`` / ``predict_proba`` interface suitable +for real-time, single-sample inference on the edge. +""" + +from __future__ import annotations + +import logging +from pathlib import Path + +import numpy as np + +logger = logging.getLogger(__name__) + + +class FraudClassifier: + """Lightweight inference wrapper around a trained XGBoost model. + + Parameters + ---------- + model_path : str + Path to the model file. The backend is chosen by extension: + - ``.json`` -- native XGBoost (``xgboost.Booster``) + - ``.onnx`` -- ONNX Runtime inference session + """ + + def __init__(self, model_path: str) -> None: + self._path = Path(model_path) + self._backend: str = "" # "xgboost" | "onnx" + self._model = None # xgboost.Booster or ort.InferenceSession + + self._load(self._path) + + # ------------------------------------------------------------------ + # Loading + # ------------------------------------------------------------------ + + def _load(self, path: Path) -> None: + """Dispatch loading to the correct backend based on file extension.""" + suffix = path.suffix.lower() + + if suffix == ".json": + self._load_xgboost(path) + elif suffix == ".onnx": + self._load_onnx(path) + else: + raise ValueError( + f"Unsupported model format '{suffix}'. " + "Expected '.json' (native XGBoost) or '.onnx'." + ) + + def _load_xgboost(self, path: Path) -> None: + """Load a native XGBoost Booster from a JSON checkpoint.""" + import xgboost as xgb + + booster = xgb.Booster() + booster.load_model(str(path)) + self._model = booster + self._backend = "xgboost" + logger.info("Loaded native XGBoost model from %s", path) + + def _load_onnx(self, path: Path) -> None: + """Load an ONNX model via ONNX Runtime.""" + import onnxruntime as ort + + sess_opts = ort.SessionOptions() + sess_opts.inter_op_num_threads = 1 + sess_opts.intra_op_num_threads = 1 + self._model = ort.InferenceSession( + str(path), sess_options=sess_opts + ) + self._backend = "onnx" + logger.info("Loaded ONNX model from %s", path) + + # ------------------------------------------------------------------ + # Inference + # ------------------------------------------------------------------ + + def predict_proba(self, features: np.ndarray) -> float: + """Return the fraud probability for a single feature vector. + + Parameters + ---------- + features : np.ndarray + 1-D feature vector (e.g. 518-dim from ``FeaturePipeline``). + + Returns + ------- + float + Probability of the positive (fraud) class in ``[0.0, 1.0]``. + """ + features = np.asarray(features, dtype=np.float32) + if features.ndim == 1: + features = features.reshape(1, -1) + + if self._backend == "xgboost": + return self._predict_xgb(features) + elif self._backend == "onnx": + return self._predict_onnx(features) + else: + raise RuntimeError("Model not loaded.") + + def predict( + self, + features: np.ndarray, + threshold: float = 0.5, + ) -> tuple[bool, float]: + """Binary prediction with confidence score. + + Parameters + ---------- + features : np.ndarray + 1-D feature vector. + threshold : float + Decision threshold. Scores >= threshold are classified as + fraud. + + Returns + ------- + tuple[bool, float] + ``(is_fraud, confidence)`` where *confidence* is the raw + fraud probability. + """ + prob = self.predict_proba(features) + return (prob >= threshold, prob) + + # ------------------------------------------------------------------ + # Backend-specific prediction + # ------------------------------------------------------------------ + + def _predict_xgb(self, features_2d: np.ndarray) -> float: + """Run prediction through the native XGBoost Booster.""" + import xgboost as xgb + + dmat = xgb.DMatrix(features_2d) + preds = self._model.predict(dmat) # shape (1,) for single sample + return float(preds[0]) + + def _predict_onnx(self, features_2d: np.ndarray) -> float: + """Run prediction through ONNX Runtime.""" + input_name = self._model.get_inputs()[0].name + output = self._model.run(None, {input_name: features_2d}) + + # ONNX XGBoost classifiers typically output: + # [0] -> predicted labels, [1] -> class probabilities + if len(output) >= 2: + # output[1] is a list of dicts [{0: p0, 1: p1}] or ndarray + probs = output[1] + if isinstance(probs, list): + # List of dicts: [{0: 0.2, 1: 0.8}] + return float(probs[0][1]) + else: + # ndarray of shape (1, 2) + return float(probs[0, 1]) + else: + # Single output: raw score (sigmoid already applied for + # binary:logistic objective) + return float(output[0][0]) diff --git a/sentinel_edge/engine.py b/sentinel_edge/engine.py new file mode 100644 index 0000000000000000000000000000000000000000..f25706a52fb92ea5782818fc4c5daac6441c78c6 --- /dev/null +++ b/sentinel_edge/engine.py @@ -0,0 +1,645 @@ +"""Top-level SentinelEdge inference engine. + +Ties together the feature pipeline, classifier, score accumulator, +and alert engine into a single, easy-to-use API for analysing SMS +messages, URLs, and phone call transcripts in real time. + +Supports three classifier back-ends: + - ``"mlp"`` -- MiniLM embeddings + MLP (federable) + - ``"xgboost"`` -- TF-IDF + XGBoost (legacy) + - ``"auto"`` -- tries MLP first, then XGBoost, then heuristic +""" + +from __future__ import annotations + +import logging +import re +import time +from dataclasses import dataclass, field +from pathlib import Path + +import numpy as np + +from sentinel_edge.classifier.alert_engine import AlertEngine, RiskLevel +from sentinel_edge.classifier.score_accumulator import ScoreAccumulator +from sentinel_edge.classifier.xgb_classifier import FraudClassifier +from sentinel_edge.features.feature_pipeline import FeaturePipeline +from sentinel_edge.features.handcrafted import extract_handcrafted_features +from sentinel_edge.features.url_features import extract_url_features +from sentinel_edge.audio.sentence_splitter import SentenceSplitter + +logger = logging.getLogger(__name__) + +# Simple heuristic patterns for auto-detection +_URL_RE = re.compile(r"https?://[^\s]+|www\.[^\s]+", re.IGNORECASE) + + +@dataclass +class DetectionResult: + """Structured output from every ``analyze_*`` method. + + Attributes + ---------- + channel : str + Detection channel: ``'sms'``, ``'url'``, or ``'call'``. + is_fraud : bool + Binary fraud verdict. + confidence : float + Fraud probability in ``[0.0, 1.0]``. + risk_level : str + One of ``'critical'``, ``'high'``, ``'medium'``, ``'low'``, + ``'safe'``. + reasons : list[str] + Human-readable explanations for the verdict. + inference_ms : float + Wall-clock latency of the prediction in milliseconds. + """ + + channel: str + is_fraud: bool + confidence: float + risk_level: str + reasons: list[str] = field(default_factory=list) + inference_ms: float = 0.0 + + +class SentinelEngine: + """High-level facade for SentinelEdge fraud detection. + + Parameters + ---------- + models_dir : str + Directory containing trained model artefacts: + + - ``call_fraud_mlp.npz`` -- MLP classifier + - ``xgb_model.json`` / ``.onnx`` -- XGBoost classifier + - ``tfidf.joblib`` -- fitted TF-IDF vectorizer + + If the directory (or individual files) does not exist, the engine + falls back to handcrafted-features-only mode so that development + and testing can proceed without trained models. + threshold : float + Decision threshold for the binary fraud prediction. + alpha : float + EMA smoothing factor for phone-call score accumulation. + pipeline : str + Classifier pipeline to use: + + - ``"auto"`` -- MLP if available, then XGBoost, then heuristic. + - ``"mlp"`` -- MiniLM embeddings + MLP. + - ``"xgboost"`` -- TF-IDF + XGBoost (legacy behaviour). + """ + + def __init__( + self, + models_dir: str = "models", + threshold: float = 0.5, + alpha: float = 0.3, + pipeline: str = "auto", + ) -> None: + self._models_dir = Path(models_dir) + self.threshold = threshold + self._pipeline_mode = pipeline + + # Resolved at init time + self._active_backend: str = "heuristic" # "mlp" | "xgboost" | "heuristic" + self.classifier = None # FraudClassifier or MLPClassifier + self.pipeline: FeaturePipeline = None # type: ignore[assignment] + + # Channel-specific classifiers (loaded separately) + self._sms_classifier = None # XGBClassifier for SMS + self._sms_pipeline: FeaturePipeline | None = None + self._url_classifier = None # XGBClassifier for URLs + + self._init_pipeline(pipeline) + self._init_sms_classifier() + self._init_url_classifier() + + # --- Score accumulator (per-call state) --- + self.accumulator = ScoreAccumulator(alpha=alpha) + + # --- Alert engine --- + self.alert_engine = AlertEngine() + + # --- Sentence splitter (reusable across calls) --- + self._splitter = SentenceSplitter() + + # ------------------------------------------------------------------ + # Pipeline initialisation + # ------------------------------------------------------------------ + + def _init_pipeline(self, pipeline: str) -> None: + """Resolve which classifier back-end to use and set up the + corresponding feature pipeline.""" + if pipeline == "mlp" or pipeline == "auto": + if self._try_load_mlp(): + return + + if pipeline == "xgboost" or pipeline == "auto": + if self._try_load_xgboost(): + return + + if pipeline not in ("auto", "mlp", "xgboost"): + raise ValueError( + f"Unknown pipeline '{pipeline}'. " + "Expected 'auto', 'mlp', or 'xgboost'." + ) + + # Heuristic fallback + logger.warning( + "No trained model found in %s -- running in " + "handcrafted-features-only mode (scores will be " + "heuristic-based).", + self._models_dir, + ) + tfidf_path = ( + self._resolve_model("tfidf_call_vectorizer.pkl") + or self._resolve_model("tfidf.joblib") + ) + self.pipeline = FeaturePipeline(tfidf_path, mode="tfidf") + self._active_backend = "heuristic" + + def _try_load_mlp(self) -> bool: + """Attempt to load the MLP classifier and embedding pipeline. + + Requires ``sentence-transformers`` for real MiniLM embeddings. + If the package is missing, falls back to XGBoost/heuristic to + avoid producing NaN scores from hash-based fallback embeddings + fed into weights trained on real embeddings. + """ + mlp_path = self._resolve_model("call_fraud_mlp.npz") + if mlp_path is None: + return False + + # Check that sentence-transformers is actually importable -- + # without it, the embedding pipeline produces hash vectors + # that are incompatible with weights trained on real MiniLM. + try: + import sentence_transformers # noqa: F401 + except ImportError: + logger.warning( + "MLP model exists at %s but sentence-transformers is not " + "installed. Skipping MLP pipeline to avoid NaN scores. " + "Install with: pip install sentence-transformers", + mlp_path, + ) + return False + + try: + from sentinel_edge.classifier.mlp_classifier import MLPClassifier + + self.classifier = MLPClassifier.load(mlp_path) + self.pipeline = FeaturePipeline(mode="embedding") + self._active_backend = "mlp" + logger.info("Using MLP pipeline (embedding + MLP classifier).") + return True + except Exception as exc: + logger.warning( + "Could not load MLP classifier from %s: %s", mlp_path, exc + ) + return False + + def _try_load_xgboost(self) -> bool: + """Attempt to load the XGBoost classifier and TF-IDF pipeline.""" + # Try multiple naming conventions for TF-IDF vectorizer + tfidf_path = ( + self._resolve_model("tfidf_call_vectorizer.pkl") + or self._resolve_model("tfidf_call_vectorizer_adversarial.pkl") + or self._resolve_model("tfidf.joblib") + ) + # Try multiple naming conventions for XGBoost model + model_candidates = [ + "call_fraud_xgb_adversarial.json", + "call_fraud_xgb.json", + "xgb_model.json", + "call_fraud_xgb.onnx", + "xgb_model.onnx", + ] + for name in model_candidates: + model_path = self._resolve_model(name) + if model_path is not None: + try: + self.classifier = FraudClassifier(model_path) + self.pipeline = FeaturePipeline(tfidf_path, mode="tfidf") + self._active_backend = "xgboost" + logger.info( + "Using XGBoost pipeline (TF-IDF + XGBoost classifier)." + ) + return True + except Exception as exc: + logger.warning( + "Could not load XGBoost model from %s: %s", + model_path, + exc, + ) + return False + + # ------------------------------------------------------------------ + # Channel-specific model loading (SMS, URL) + # ------------------------------------------------------------------ + + def _init_sms_classifier(self) -> None: + """Load the SMS-specific XGBoost model and TF-IDF vectorizer.""" + model_path = self._resolve_model("sms_fraud_xgb.json") + tfidf_path = self._resolve_model("tfidf_sms_vectorizer.pkl") + + if model_path is None: + logger.debug("SMS model sms_fraud_xgb.json not found in %s", self._models_dir) + return + + try: + import xgboost as xgb + + model = xgb.XGBClassifier() + model.load_model(model_path) + self._sms_classifier = model + + # Load the SMS-specific TF-IDF vectorizer into a FeaturePipeline + from sentinel_edge.features.tfidf import TfidfFeatureExtractor + + if tfidf_path is not None: + sms_tfidf = TfidfFeatureExtractor(tfidf_path) + else: + sms_tfidf = TfidfFeatureExtractor() + + self._sms_pipeline = FeaturePipeline(tfidf_path, mode="tfidf") + logger.info( + "Loaded SMS fraud classifier from %s (tfidf: %s)", + model_path, tfidf_path or "default", + ) + except Exception as exc: + logger.warning("Could not load SMS classifier: %s", exc) + self._sms_classifier = None + self._sms_pipeline = None + + def _init_url_classifier(self) -> None: + """Load the URL-specific XGBoost model.""" + model_path = self._resolve_model("url_fraud_xgb.json") + + if model_path is None: + logger.debug("URL model url_fraud_xgb.json not found in %s", self._models_dir) + return + + try: + import xgboost as xgb + + model = xgb.XGBClassifier() + model.load_model(model_path) + self._url_classifier = model + logger.info("Loaded URL fraud classifier from %s", model_path) + except Exception as exc: + logger.warning("Could not load URL classifier: %s", exc) + self._url_classifier = None + + # ------------------------------------------------------------------ + # Channel-specific analysis + # ------------------------------------------------------------------ + + def analyze_sms(self, text: str) -> DetectionResult: + """Analyse an SMS / text message for fraud indicators. + + Uses the dedicated SMS XGBoost classifier if available, + otherwise falls back to the general pipeline. + + Parameters + ---------- + text : str + The full SMS body. + + Returns + ------- + DetectionResult + """ + t0 = time.perf_counter() + + features = extract_handcrafted_features(text) + + if self._sms_classifier is not None and self._sms_pipeline is not None: + # Use the dedicated SMS classifier + feature_vec = self._sms_pipeline.extract(text) + feature_vec = np.asarray(feature_vec, dtype=np.float32).reshape(1, -1) + proba = self._sms_classifier.predict_proba(feature_vec) + # predict_proba returns shape (n_samples, n_classes) for XGBClassifier + score = float(proba[0, 1]) if proba.ndim == 2 else float(proba[0]) + else: + # BUG-MARKER: SMS classifier fallback -- no dedicated SMS model. + logger.warning( + "HEURISTIC FALLBACK: No SMS classifier loaded, using general " + "pipeline. Load models/sms_fraud_xgb.json for accurate SMS " + "detection." + ) + score, features = self.analyze_sentence(text) + + alert = self.alert_engine.evaluate(score, features) + elapsed_ms = (time.perf_counter() - t0) * 1000.0 + + return DetectionResult( + channel="sms", + is_fraud=score >= self.threshold, + confidence=score, + risk_level=alert.risk_level.value, + reasons=alert.reasons, + inference_ms=elapsed_ms, + ) + + def analyze_url(self, url: str) -> DetectionResult: + """Analyse a URL for phishing indicators. + + Uses the dedicated URL XGBoost classifier if available, + otherwise falls back to heuristic scoring from URL features. + + Parameters + ---------- + url : str + The URL to evaluate. + + Returns + ------- + DetectionResult + """ + t0 = time.perf_counter() + + url_feats = extract_url_features(url) + + if self._url_classifier is not None: + # Use the dedicated URL classifier with the training-time + # feature extraction (from train_url_classifier.py). + from training.train_url_classifier import ( + extract_url_features as extract_train_url_features, + ) + + train_feats = extract_train_url_features(url) + feature_vec = np.array( + list(train_feats.values()), dtype=np.float32 + ).reshape(1, -1) + proba = self._url_classifier.predict_proba(feature_vec) + score = float(proba[0, 1]) if proba.ndim == 2 else float(proba[0]) + else: + # BUG-MARKER: URL heuristic fallback -- no trained URL model. + logger.warning( + "HEURISTIC FALLBACK: No URL classifier loaded, scoring with " + "structural features only. Load models/url_fraud_xgb.json " + "for accurate detection." + ) + score = self._heuristic_url_score(url_feats) + + reasons: list[str] = [] + if url_feats["has_ip"]: + reasons.append("URL uses a raw IP address instead of a domain") + if url_feats["suspicious_tld"]: + reasons.append("URL uses a suspicious top-level domain") + if not url_feats["has_https"]: + reasons.append("URL does not use HTTPS") + if url_feats["domain_entropy"] > 4.0: + reasons.append("Domain name has high entropy (possibly generated)") + if url_feats["at_symbol"]: + reasons.append("URL contains an @ symbol (possible credential trick)") + if url_feats["subdomain_count"] > 3: + reasons.append("Excessive subdomains detected") + if url_feats["url_length"] > 75: + reasons.append("Unusually long URL") + if url_feats["has_port"]: + reasons.append("Non-standard port in URL") + + risk = self.alert_engine._classify_risk(score) + elapsed_ms = (time.perf_counter() - t0) * 1000.0 + + return DetectionResult( + channel="url", + is_fraud=score >= self.threshold, + confidence=score, + risk_level=risk.value, + reasons=reasons, + inference_ms=elapsed_ms, + ) + + def analyze_call(self, transcript: str) -> DetectionResult: + """Analyse a full or partial phone call transcript. + + The transcript is split into sentences, each scored individually, + and the EMA accumulator aggregates the per-sentence scores. + + Parameters + ---------- + transcript : str + Full or incremental transcript text. + + Returns + ------- + DetectionResult + """ + t0 = time.perf_counter() + + self._splitter.reset() + sentences = self._splitter.feed(transcript) + leftover = self._splitter.flush() + if leftover: + sentences.append(leftover) + + all_features: dict[str, float] = {} + for sentence in sentences: + score, features = self.analyze_sentence(sentence) + self.accumulator.update(score) + # Merge features (keep the max across sentences for each key) + for k, v in features.items(): + all_features[k] = max(all_features.get(k, 0.0), v) + + ema_score = self.accumulator.current_score + alert = self.alert_engine.evaluate(ema_score, all_features) + elapsed_ms = (time.perf_counter() - t0) * 1000.0 + + return DetectionResult( + channel="call", + is_fraud=ema_score >= self.threshold, + confidence=ema_score, + risk_level=alert.risk_level.value, + reasons=alert.reasons, + inference_ms=elapsed_ms, + ) + + # ------------------------------------------------------------------ + # Sentence-level analysis + # ------------------------------------------------------------------ + + def analyze_sentence( + self, sentence: str + ) -> tuple[float, dict[str, float]]: + """Score a single sentence and return raw features. + + Parameters + ---------- + sentence : str + One sentence of text. + + Returns + ------- + tuple[float, dict[str, float]] + ``(fraud_probability, handcrafted_features)``. + """ + features = self.pipeline.extract_handcrafted_only(sentence) + + if self.classifier is not None: + feature_vec = self.pipeline.extract(sentence) + score = self.classifier.predict_proba(feature_vec) + else: + # BUG-MARKER: Heuristic fallback active -- no trained model loaded. + # This produces lower-quality scores. If you see this in logs, + # check that model files exist in the models/ directory. + logger.warning( + "HEURISTIC FALLBACK: No classifier loaded, scoring with " + "handcrafted features only. Results will be degraded. " + "Ensure model files exist in %s", + self._models_dir, + ) + score = self._heuristic_text_score(features) + + return score, features + + # ------------------------------------------------------------------ + # Auto-detection + # ------------------------------------------------------------------ + + def analyze_auto(self, text: str) -> DetectionResult: + """Auto-detect the channel and analyse accordingly. + + Heuristic: + - If the text looks like a URL (starts with ``http`` / ``www`` + and is a single token), use ``analyze_url``. + - Otherwise, use ``analyze_sms`` (which also works well for + single transcript sentences). + + Parameters + ---------- + text : str + Input text to analyse. + + Returns + ------- + DetectionResult + """ + stripped = text.strip() + + # Single-token URL check + if ( + " " not in stripped + and _URL_RE.match(stripped) + ): + return self.analyze_url(stripped) + + return self.analyze_sms(stripped) + + # ------------------------------------------------------------------ + # Call-state management + # ------------------------------------------------------------------ + + def reset_call_state(self) -> None: + """Reset the EMA accumulator for a new phone call.""" + self.accumulator.reset() + + # ------------------------------------------------------------------ + # Introspection + # ------------------------------------------------------------------ + + @property + def active_backend(self) -> str: + """Return the name of the active classifier back-end. + + One of ``"mlp"``, ``"xgboost"``, or ``"heuristic"``. + """ + return self._active_backend + + # ------------------------------------------------------------------ + # Heuristic fallback scorers + # ------------------------------------------------------------------ + + @staticmethod + def _heuristic_text_score(features: dict[str, float]) -> float: + """Produce a rough fraud score from handcrafted features alone. + + This is used only when no trained classifier is loaded. Weights + are calibrated so that a single strong signal (e.g. 3 urgency + words + 2 financial words + a threat) reaches the 0.5 threshold, + and multiple converging signals push well above it. + """ + score = 0.0 + # High-signal features (impersonation + urgency are the strongest + # discriminators on real data -- see real_data_results.txt) + score += min(features.get("impersonation_count", 0) * 0.15, 0.45) + score += min(features.get("urgency_count", 0) * 0.12, 0.36) + score += min(features.get("financial_count", 0) * 0.10, 0.30) + score += min(features.get("action_count", 0) * 0.08, 0.24) + + # Binary pattern features + score += features.get("has_threat", 0) * 0.15 + score += features.get("has_prize", 0) * 0.12 + score += features.get("has_verify_pattern", 0) * 0.10 + score += features.get("has_account_ref", 0) * 0.08 + score += features.get("has_shortened_url", 0) * 0.08 + score += features.get("has_url", 0) * 0.05 + score += features.get("dollar_sign", 0) * 0.05 + score += features.get("has_phone_number", 0) * 0.03 + + # Apply sigmoid-like compression so scores spread across [0, 1] + # instead of clustering near 0. Raw sum can exceed 1.0 with + # multiple signals, the sigmoid maps it to (0, 1). + if score > 0: + score = 1.0 / (1.0 + np.exp(-4.0 * (score - 0.4))) + + return float(np.clip(score, 0.0, 1.0)) + + @staticmethod + def _heuristic_url_score(url_feats: dict[str, float]) -> float: + """Produce a rough phishing score from URL features alone.""" + score = 0.0 + score += url_feats.get("has_ip", 0) * 0.20 + score += url_feats.get("suspicious_tld", 0) * 0.15 + score += (1.0 - url_feats.get("has_https", 0)) * 0.10 + score += url_feats.get("at_symbol", 0) * 0.15 + + entropy = url_feats.get("domain_entropy", 0) + if entropy > 4.0: + score += min((entropy - 4.0) * 0.05, 0.15) + + subdomain_count = url_feats.get("subdomain_count", 0) + if subdomain_count > 3: + score += min((subdomain_count - 3) * 0.05, 0.10) + + url_length = url_feats.get("url_length", 0) + if url_length > 75: + score += min((url_length - 75) * 0.002, 0.10) + + score += url_feats.get("has_port", 0) * 0.05 + + return min(max(score, 0.0), 1.0) + + # ------------------------------------------------------------------ + # Internal helpers + # ------------------------------------------------------------------ + + def _resolve_model(self, filename: str) -> str | None: + """Return the full path to a model file if it exists, else None. + + Supports legacy artifact names so training outputs can be used + without manual renaming. + """ + aliases = { + "tfidf.joblib": "tfidf_call_vectorizer.pkl", + "xgb_model.json": "call_fraud_xgb.json", + } + + path = self._models_dir / filename + if path.exists(): + return str(path) + + alias = aliases.get(filename) + if alias is not None: + alias_path = self._models_dir / alias + if alias_path.exists(): + logger.info( + "Using legacy model artifact %s for %s", + alias, + filename, + ) + return str(alias_path) + + return None diff --git a/sentinel_edge/features/__init__.py b/sentinel_edge/features/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..339e95fe548bfe657c96ecb2755555088a3fd502 --- /dev/null +++ b/sentinel_edge/features/__init__.py @@ -0,0 +1 @@ +"""Feature extraction for fraud detection.""" diff --git a/sentinel_edge/features/embedding.py b/sentinel_edge/features/embedding.py new file mode 100644 index 0000000000000000000000000000000000000000..bb9de6c23d214d536571444975d98f3e024bd402 --- /dev/null +++ b/sentinel_edge/features/embedding.py @@ -0,0 +1,161 @@ +"""Sentence embedding feature extractor using all-MiniLM-L6-v2. + +Produces 384-dimensional dense embeddings that capture semantic meaning, +replacing sparse 500-dim TF-IDF vectors. Falls back to a deterministic +hash-based embedding when sentence-transformers is unavailable. +""" + +from __future__ import annotations + +import hashlib +import logging + +import numpy as np + +logger = logging.getLogger(__name__) + +EMBEDDING_DIM = 384 + + +class EmbeddingExtractor: + """Dense sentence embedding extractor. + + Uses the ``all-MiniLM-L6-v2`` sentence-transformer model for + high-quality 384-dim embeddings. If the model cannot be loaded + (missing dependency or download failure) a deterministic hash-based + fallback is used so the pipeline never crashes. + + Parameters + ---------- + model_name : str + HuggingFace model identifier for the sentence-transformer. + """ + + def __init__(self, model_name: str = "all-MiniLM-L6-v2") -> None: + self.model_name = model_name + self._model = None + self._is_fallback = False + + # ------------------------------------------------------------------ + # Lazy loading + # ------------------------------------------------------------------ + + def _load(self) -> None: + """Lazy-load the sentence-transformers model.""" + if self._model is not None or self._is_fallback: + return + try: + from sentence_transformers import SentenceTransformer + + self._model = SentenceTransformer(self.model_name) + logger.info( + "Loaded sentence-transformer model '%s'", self.model_name + ) + except Exception as exc: + logger.warning( + "Could not load sentence-transformer '%s': %s " + "-- using deterministic hash fallback (non-semantic).", + self.model_name, + exc, + ) + self._is_fallback = True + + # ------------------------------------------------------------------ + # Public API + # ------------------------------------------------------------------ + + def encode(self, text: str) -> np.ndarray: + """Encode a single sentence to a 384-dim float32 vector. + + Parameters + ---------- + text : str + Input sentence. + + Returns + ------- + np.ndarray + 1-D array of shape ``(384,)`` with dtype ``float32``. + """ + self._load() + if self._is_fallback: + return self._fallback_encode(text) + return self._model.encode(text, convert_to_numpy=True).astype( + np.float32 + ) + + def encode_batch(self, texts: list[str]) -> np.ndarray: + """Encode a batch of sentences. + + Parameters + ---------- + texts : list[str] + List of input sentences. + + Returns + ------- + np.ndarray + 2-D array of shape ``(len(texts), 384)`` with dtype ``float32``. + """ + self._load() + if self._is_fallback: + return np.array( + [self._fallback_encode(t) for t in texts], dtype=np.float32 + ) + return self._model.encode( + texts, convert_to_numpy=True, show_progress_bar=len(texts) > 100 + ).astype(np.float32) + + # ------------------------------------------------------------------ + # Fallback embedding + # ------------------------------------------------------------------ + + @staticmethod + def _fallback_encode(text: str) -> np.ndarray: + """Deterministic hash-based embedding (non-semantic). + + Produces a normalised 384-dim float32 vector from the SHA-512 + hash of the lowercased input. This is NOT a semantic embedding + but allows the full pipeline to run without the heavy + ``sentence-transformers`` dependency. + + Parameters + ---------- + text : str + Input sentence. + + Returns + ------- + np.ndarray + Unit-norm 1-D array of shape ``(384,)``. + """ + seed = text.lower().encode("utf-8") + h = hashlib.sha512(seed).digest() + + # Extend the hash to fill 384 float32 values (384 * 4 = 1536 bytes) + extended = b"" + for i in range(24): # 24 * 64 bytes = 1536 bytes + extended += hashlib.sha512(h + i.to_bytes(4, "big")).digest() + + arr = np.frombuffer(extended[: EMBEDDING_DIM * 4], dtype=np.float32).copy() + + # Normalise to unit vector + norm = np.linalg.norm(arr) + if norm > 0: + arr /= norm + return arr + + # ------------------------------------------------------------------ + # Properties + # ------------------------------------------------------------------ + + @property + def embedding_dim(self) -> int: + """Dimensionality of the output embeddings.""" + return EMBEDDING_DIM + + @property + def using_fallback(self) -> bool: + """Whether the extractor is using the hash-based fallback.""" + self._load() + return self._is_fallback diff --git a/sentinel_edge/features/feature_pipeline.py b/sentinel_edge/features/feature_pipeline.py new file mode 100644 index 0000000000000000000000000000000000000000..e1ceb071548afaa62460d4a55a9f6468057350e7 --- /dev/null +++ b/sentinel_edge/features/feature_pipeline.py @@ -0,0 +1,180 @@ +"""Unified feature pipeline combining handcrafted and text features. + +Supports two modes: + - ``"tfidf"`` (default, backward compatible): + 18 handcrafted + 500 TF-IDF = 518 dimensions + - ``"embedding"``: + 18 handcrafted + 384 MiniLM sentence embeddings = 402 dimensions +""" + +from __future__ import annotations + +import numpy as np + +from sentinel_edge.features.handcrafted import extract_handcrafted_features +from sentinel_edge.features.tfidf import TfidfFeatureExtractor + +# Dimensionality constants +HANDCRAFTED_DIM = 18 +TFIDF_DIM = 500 +EMBEDDING_DIM = 384 +TFIDF_TOTAL_DIM = HANDCRAFTED_DIM + TFIDF_DIM # 518 +EMBEDDING_TOTAL_DIM = HANDCRAFTED_DIM + EMBEDDING_DIM # 402 + +# Backward-compatible aliases +TOTAL_DIM = TFIDF_TOTAL_DIM + + +class FeaturePipeline: + """Combines handcrafted and text features into a single vector. + + Parameters + ---------- + tfidf_path : str | None + Path to a pre-fitted TF-IDF vectorizer (joblib). If *None* the + TF-IDF component will be initialised un-fitted and will return + zero vectors until ``fit_tfidf`` is called. + mode : str + Feature extraction mode: + - ``"tfidf"`` -- handcrafted (18) + TF-IDF (500) = 518 dims + - ``"embedding"`` -- handcrafted (18) + MiniLM (384) = 402 dims + embedding_model : str + Sentence-transformer model name (only used when ``mode="embedding"``). + """ + + def __init__( + self, + tfidf_path: str | None = None, + mode: str = "tfidf", + embedding_model: str = "all-MiniLM-L6-v2", + ) -> None: + if mode not in ("tfidf", "embedding"): + raise ValueError( + f"Unknown mode '{mode}'. Expected 'tfidf' or 'embedding'." + ) + self.mode = mode + self._embedding_model_name = embedding_model + + # TF-IDF component (always initialised for backward compat) + self.tfidf = TfidfFeatureExtractor(tfidf_path) + + # Embedding component (lazy-loaded only when needed) + self._embedder = None + + # ------------------------------------------------------------------ + # Lazy embedding loader + # ------------------------------------------------------------------ + + def _get_embedder(self): + """Return the EmbeddingExtractor, creating it on first use.""" + if self._embedder is None: + from sentinel_edge.features.embedding import EmbeddingExtractor + + self._embedder = EmbeddingExtractor(self._embedding_model_name) + return self._embedder + + # ------------------------------------------------------------------ + # Public API + # ------------------------------------------------------------------ + + def extract(self, text: str) -> np.ndarray: + """Produce a feature vector for *text*. + + The dimensionality depends on the configured mode: + - ``"tfidf"``: 518 dims (18 handcrafted + 500 TF-IDF) + - ``"embedding"``: 402 dims (18 handcrafted + 384 embeddings) + + Parameters + ---------- + text : str + A single document (SMS, transcript sentence, etc.). + + Returns + ------- + np.ndarray + 1-D float array. + """ + handcrafted = extract_handcrafted_features(text) + handcrafted_arr = np.array( + list(handcrafted.values()), dtype=np.float64 + ) + + if self.mode == "embedding": + embedder = self._get_embedder() + emb = embedder.encode(text).astype(np.float64) + return np.concatenate([handcrafted_arr, emb]) + else: + tfidf_feats = self.tfidf.transform(text) + return np.concatenate([handcrafted_arr, tfidf_feats]) + + def extract_batch(self, texts: list[str]) -> np.ndarray: + """Extract features for a batch of texts. + + Parameters + ---------- + texts : list[str] + List of documents. + + Returns + ------- + np.ndarray + 2-D float32 array of shape ``(len(texts), feature_dim)``. + """ + # Handcrafted features for all texts + hc_list = [] + for t in texts: + feats = extract_handcrafted_features(t) + hc_list.append(list(feats.values())) + hc_matrix = np.array(hc_list, dtype=np.float32) + + if self.mode == "embedding": + embedder = self._get_embedder() + emb_matrix = embedder.encode_batch(texts) + return np.hstack([hc_matrix, emb_matrix]) + else: + tfidf_matrix = np.zeros( + (len(texts), self.tfidf.n_features), dtype=np.float32 + ) + for i, t in enumerate(texts): + tfidf_matrix[i] = self.tfidf.transform(t).astype(np.float32) + return np.hstack([hc_matrix, tfidf_matrix]) + + def extract_handcrafted_only(self, text: str) -> dict[str, float]: + """Return only the 18 handcrafted features as a dict. + + Useful for interpretability: the alert engine generates + human-readable reasons from these named features. + """ + return extract_handcrafted_features(text) + + def fit_tfidf(self, texts: list[str]) -> None: + """Fit the TF-IDF component on a training corpus. + + Parameters + ---------- + texts : list[str] + Training documents. + """ + self.tfidf.fit(texts) + + def save_tfidf(self, path: str) -> None: + """Persist the fitted TF-IDF vectorizer to *path*.""" + self.tfidf.save(path) + + @property + def feature_dim(self) -> int: + """Total feature vector dimensionality for the current mode.""" + if self.mode == "embedding": + return EMBEDDING_TOTAL_DIM + return TFIDF_TOTAL_DIM + + @property + def is_ready(self) -> bool: + """True when the pipeline is ready for feature extraction. + + - In ``"tfidf"`` mode: requires a fitted TF-IDF vectorizer. + - In ``"embedding"`` mode: always ready (embedder lazy-loads). + """ + if self.mode == "embedding": + return True + return self.tfidf.is_fitted diff --git a/sentinel_edge/features/handcrafted.py b/sentinel_edge/features/handcrafted.py new file mode 100644 index 0000000000000000000000000000000000000000..057be42905f7a8857ac78c15bbe622cf514196e3 --- /dev/null +++ b/sentinel_edge/features/handcrafted.py @@ -0,0 +1,166 @@ +"""Handcrafted feature extraction for fraud detection. + +Extracts 18 linguistic and structural features from a single sentence +of transcript text, designed to capture common phone/SMS fraud patterns. +""" + +from __future__ import annotations + +import re +from typing import Any + + +# --------------------------------------------------------------------------- +# Word lists for categorical counting +# --------------------------------------------------------------------------- + +_URGENCY_WORDS: list[str] = [ + "urgent", "immediately", "now", "act", "deadline", "expire", + "hurry", "quickly", "asap", "right away", "limited time", "last chance", +] + +_ACTION_WORDS: list[str] = [ + "click", "verify", "confirm", "validate", "login", "update", + "provide", "submit", "enter", "activate", "download", "install", +] + +_FINANCIAL_WORDS: list[str] = [ + "bank", "account", "credit", "payment", "wire", "transfer", + "routing", "deposit", "withdrawal", "balance", "transaction", "invoice", +] + +_IMPERSONATION_WORDS: list[str] = [ + "irs", "fbi", "ssa", "microsoft", "apple", "amazon", + "google", "police", "government", "federal", "agent", "department", + "officer", +] + +# --------------------------------------------------------------------------- +# Pre-compiled regex patterns +# --------------------------------------------------------------------------- + +_URL_PATTERN = re.compile( + r"https?://[^\s]+|www\.[^\s]+", re.IGNORECASE +) + +_SHORTENED_URL_PATTERN = re.compile( + r"(?:bit\.ly|tinyurl\.com|t\.co|goo\.gl|ow\.ly|is\.gd|buff\.ly" + r"|rebrand\.ly|cutt\.ly|shorturl\.at)[/\s]?", + re.IGNORECASE, +) + +_VERIFY_PATTERN = re.compile(r"\b(?:verify|confirm|validate)\b", re.IGNORECASE) +_THREAT_PATTERN = re.compile( + r"\b(?:arrest|suspend|terminate|cancel|lock|seize|revoke|prosecute)", + re.IGNORECASE, +) +_PRIZE_PATTERN = re.compile( + r"\b(?:won|winner|prize|congratulations|selected|lucky|reward)\b", + re.IGNORECASE, +) +_ACCOUNT_REF_PATTERN = re.compile( + r"\b(?:account|password|pin|ssn|social\s+security|credentials)\b", + re.IGNORECASE, +) +_PHONE_NUMBER_PATTERN = re.compile( + r"(?:\+?1[-.\s]?)?" # optional country code + r"(?:\(?\d{3}\)?[-.\s]?)" # area code + r"\d{3}[-.\s]?\d{4}" # subscriber number +) + + +# --------------------------------------------------------------------------- +# Helper: count occurrences of words/phrases in text +# --------------------------------------------------------------------------- + +def _count_word_occurrences(text_lower: str, word_list: list[str]) -> int: + """Count how many times any word in *word_list* appears in *text_lower*. + + Multi-word phrases (e.g. "right away") are matched as substrings. + Single words are matched on word boundaries to avoid partial matches. + """ + count = 0 + for word in word_list: + if " " in word: + # Multi-word phrase: simple substring count + count += text_lower.count(word) + else: + # Single word: use word boundary regex for precision + count += len(re.findall(rf"\b{re.escape(word)}\b", text_lower)) + return count + + +# --------------------------------------------------------------------------- +# Public API +# --------------------------------------------------------------------------- + +def extract_handcrafted_features(text: str) -> dict[str, float]: + """Extract all 18 handcrafted features from a single sentence. + + Parameters + ---------- + text : str + A single sentence (or short text segment) from a transcript. + + Returns + ------- + dict[str, float] + Dictionary mapping feature names to their numeric values. + Binary features are represented as 0.0 or 1.0. + """ + text_lower = text.lower() + + # --- Count-based features --- + urgency_count = _count_word_occurrences(text_lower, _URGENCY_WORDS) + action_count = _count_word_occurrences(text_lower, _ACTION_WORDS) + financial_count = _count_word_occurrences(text_lower, _FINANCIAL_WORDS) + impersonation_count = _count_word_occurrences(text_lower, _IMPERSONATION_WORDS) + + # --- Binary pattern features --- + urls_found = _URL_PATTERN.findall(text) + has_url = 1.0 if urls_found else 0.0 + has_shortened_url = 1.0 if _SHORTENED_URL_PATTERN.search(text_lower) else 0.0 + has_verify_pattern = 1.0 if _VERIFY_PATTERN.search(text) else 0.0 + has_threat = 1.0 if _THREAT_PATTERN.search(text) else 0.0 + has_prize = 1.0 if _PRIZE_PATTERN.search(text) else 0.0 + has_account_ref = 1.0 if _ACCOUNT_REF_PATTERN.search(text) else 0.0 + dollar_sign = 1.0 if "$" in text else 0.0 + has_phone_number = 1.0 if _PHONE_NUMBER_PATTERN.search(text) else 0.0 + + # --- Structural / statistical features --- + char_count = len(text) + words = text.split() + word_count = len(words) + avg_word_len = ( + sum(len(w) for w in words) / word_count if word_count > 0 else 0.0 + ) + url_count = len(urls_found) + exclamation_count = text.count("!") + + alpha_chars = [c for c in text if c.isalpha()] + caps_ratio = ( + sum(1 for c in alpha_chars if c.isupper()) / len(alpha_chars) + if alpha_chars + else 0.0 + ) + + return { + "urgency_count": float(urgency_count), + "action_count": float(action_count), + "financial_count": float(financial_count), + "impersonation_count": float(impersonation_count), + "has_url": has_url, + "has_shortened_url": has_shortened_url, + "has_verify_pattern": has_verify_pattern, + "has_threat": has_threat, + "has_prize": has_prize, + "has_account_ref": has_account_ref, + "dollar_sign": dollar_sign, + "has_phone_number": has_phone_number, + "char_count": float(char_count), + "word_count": float(word_count), + "avg_word_len": avg_word_len, + "url_count": float(url_count), + "exclamation_count": float(exclamation_count), + "caps_ratio": caps_ratio, + } diff --git a/sentinel_edge/features/tfidf.py b/sentinel_edge/features/tfidf.py new file mode 100644 index 0000000000000000000000000000000000000000..0be4eeb437b9ed3622f870c318358bd8fc6b8c61 --- /dev/null +++ b/sentinel_edge/features/tfidf.py @@ -0,0 +1,160 @@ +"""TF-IDF feature extraction wrapper. + +Wraps scikit-learn's TfidfVectorizer with persistence (joblib) and a +convenient single-text transform interface for the inference pipeline. +""" + +from __future__ import annotations + +import logging +from pathlib import Path + +import joblib +import numpy as np +from sklearn.feature_extraction.text import TfidfVectorizer + +logger = logging.getLogger(__name__) + +# Default hyper-parameters shared between fit and fallback init +_DEFAULT_MAX_FEATURES = 500 +_DEFAULT_NGRAM_RANGE = (1, 2) +_DEFAULT_MIN_DF = 2 +_DEFAULT_SUBLINEAR_TF = True + + +class TfidfFeatureExtractor: + """Thin wrapper around ``TfidfVectorizer`` for the SentinelEdge pipeline. + + Parameters + ---------- + model_path : str | None + Path to a joblib-serialised ``TfidfVectorizer``. If provided the + vectorizer is loaded immediately and is ready for ``transform``. + If *None*, a default (un-fitted) vectorizer is created -- you must + call ``fit`` before ``transform``. + """ + + def __init__(self, model_path: str | None = None) -> None: + self._fitted = False + + if model_path is not None: + path = Path(model_path) + if path.exists(): + self.vectorizer: TfidfVectorizer = joblib.load(path) + self._fitted = True + logger.info("Loaded TF-IDF vectorizer from %s", model_path) + else: + logger.warning( + "TF-IDF model path %s does not exist; creating default vectorizer", + model_path, + ) + self.vectorizer = self._make_default_vectorizer() + else: + self.vectorizer = self._make_default_vectorizer() + + # ------------------------------------------------------------------ + # Public API + # ------------------------------------------------------------------ + + def fit(self, texts: list[str]) -> None: + """Fit the vectorizer on a corpus of texts. + + Parameters + ---------- + texts : list[str] + Training corpus. Each element is one document (e.g. a full + SMS or a single transcript sentence). + """ + self.vectorizer = self._make_default_vectorizer() + self.vectorizer.fit(texts) + self._fitted = True + logger.info( + "Fitted TF-IDF vectorizer on %d documents (%d features)", + len(texts), + len(self.vectorizer.vocabulary_), + ) + + def transform(self, text: str) -> np.ndarray: + """Transform a single text into a TF-IDF feature vector. + + Parameters + ---------- + text : str + A single document string. + + Returns + ------- + np.ndarray + Dense 1-D array of length ``max_features`` (default 500). + If the vectorizer has not been fitted, returns a zero vector + so that downstream code does not crash during cold-start. + """ + if not self._fitted: + logger.warning( + "TF-IDF vectorizer is not fitted; returning zero vector" + ) + return np.zeros(_DEFAULT_MAX_FEATURES, dtype=np.float64) + + sparse_vec = self.vectorizer.transform([text]) + return np.asarray(sparse_vec.toarray()).flatten() + + def save(self, path: str) -> None: + """Persist the fitted vectorizer to disk via joblib. + + Parameters + ---------- + path : str + Destination file path (e.g. ``models/tfidf.joblib``). + """ + if not self._fitted: + raise RuntimeError("Cannot save an un-fitted vectorizer.") + dest = Path(path) + dest.parent.mkdir(parents=True, exist_ok=True) + joblib.dump(self.vectorizer, dest) + logger.info("Saved TF-IDF vectorizer to %s", path) + + @classmethod + def load(cls, path: str) -> "TfidfFeatureExtractor": + """Class-method shortcut to instantiate from a saved model. + + Parameters + ---------- + path : str + Path to a joblib-serialised ``TfidfVectorizer``. + + Returns + ------- + TfidfFeatureExtractor + Ready-to-use extractor instance. + """ + return cls(model_path=path) + + # ------------------------------------------------------------------ + # Properties + # ------------------------------------------------------------------ + + @property + def is_fitted(self) -> bool: + """Whether the vectorizer has been fitted or loaded.""" + return self._fitted + + @property + def n_features(self) -> int: + """Number of output features (vocabulary size, capped by max_features).""" + if self._fitted: + return len(self.vectorizer.vocabulary_) + return _DEFAULT_MAX_FEATURES + + # ------------------------------------------------------------------ + # Internals + # ------------------------------------------------------------------ + + @staticmethod + def _make_default_vectorizer() -> TfidfVectorizer: + """Create a new un-fitted vectorizer with the project defaults.""" + return TfidfVectorizer( + max_features=_DEFAULT_MAX_FEATURES, + ngram_range=_DEFAULT_NGRAM_RANGE, + min_df=_DEFAULT_MIN_DF, + sublinear_tf=_DEFAULT_SUBLINEAR_TF, + ) diff --git a/sentinel_edge/features/url_features.py b/sentinel_edge/features/url_features.py new file mode 100644 index 0000000000000000000000000000000000000000..506a6bbe4f99bc88815e657753cdfa388ebb5093 --- /dev/null +++ b/sentinel_edge/features/url_features.py @@ -0,0 +1,150 @@ +"""URL-specific feature extraction for phishing URL detection. + +Extracts 14 structural features from a URL string that are commonly +indicative of phishing or malicious links. +""" + +from __future__ import annotations + +import math +import re +from collections import Counter +from urllib.parse import urlparse, parse_qs + + +# Suspicious TLDs frequently abused for phishing +_SUSPICIOUS_TLDS: frozenset[str] = frozenset({ + ".tk", ".ml", ".ga", ".cf", ".gq", # Freenom free TLDs + ".xyz", ".top", ".buzz", ".club", ".work", + ".info", ".zip", ".mov", ".su", ".pw", + ".cc", ".ws", ".icu", ".cyou", ".rest", +}) + +# Regex for detecting raw IP addresses in the hostname +_IP_PATTERN = re.compile( + r"^(?:\d{1,3}\.){3}\d{1,3}$" # IPv4 + r"|^\[?[0-9a-fA-F:]+\]?$" # IPv6 (with optional brackets) +) + + +def _shannon_entropy(s: str) -> float: + """Compute Shannon entropy (in bits) of a string. + + Parameters + ---------- + s : str + Input string (e.g. the domain portion of a URL). + + Returns + ------- + float + Entropy value. Higher values suggest randomised domain names + often used by DGAs (domain generation algorithms). + """ + if not s: + return 0.0 + freq = Counter(s) + length = len(s) + return -sum( + (count / length) * math.log2(count / length) + for count in freq.values() + ) + + +def extract_url_features(url: str) -> dict[str, float]: + """Extract 14 structural features from a URL. + + Parameters + ---------- + url : str + A URL string. If no scheme is present ``https://`` is assumed. + + Returns + ------- + dict[str, float] + Feature name -> numeric value mapping. + """ + # Normalise: ensure a scheme exists so urlparse works correctly. + normalised = url.strip() + if not re.match(r"^[a-zA-Z]+://", normalised): + normalised = "https://" + normalised + + parsed = urlparse(normalised) + + hostname = parsed.hostname or "" + path = parsed.path or "" + + # --- 1. url_length --- + url_length = float(len(url)) + + # --- 2. dot_count --- + dot_count = float(hostname.count(".")) + + # --- 3. hyphen_count --- + hyphen_count = float(hostname.count("-")) + + # --- 4. at_symbol --- + at_symbol = 1.0 if "@" in url else 0.0 + + # --- 5. has_ip --- + has_ip = 1.0 if _IP_PATTERN.match(hostname) else 0.0 + + # --- 6. digit_ratio --- + alpha_digits = [c for c in hostname if c.isalnum()] + digit_ratio = ( + sum(1 for c in alpha_digits if c.isdigit()) / len(alpha_digits) + if alpha_digits + else 0.0 + ) + + # --- 7. subdomain_count --- + # "www.sub.example.com" -> parts = ["www", "sub", "example", "com"] + # subdomains = total parts - 2 (domain + TLD), but at least 0 + parts = hostname.split(".") if hostname else [] + subdomain_count = float(max(len(parts) - 2, 0)) + + # --- 8. path_depth --- + # "/a/b/c" -> depth = 3 + path_segments = [seg for seg in path.split("/") if seg] + path_depth = float(len(path_segments)) + + # --- 9. has_https --- + has_https = 1.0 if parsed.scheme.lower() == "https" else 0.0 + + # --- 10. domain_entropy --- + domain_entropy = _shannon_entropy(hostname) + + # --- 11. suspicious_tld --- + suspicious_tld = 0.0 + if hostname: + last_dot = hostname.rfind(".") + if last_dot != -1: + tld = hostname[last_dot:] + suspicious_tld = 1.0 if tld.lower() in _SUSPICIOUS_TLDS else 0.0 + + # --- 12. has_port --- + has_port = 1.0 if parsed.port is not None else 0.0 + + # --- 13. query_param_count --- + query_params = parse_qs(parsed.query) + query_param_count = float(len(query_params)) + + # --- 14. fragment_present --- + fragment_present = 1.0 if parsed.fragment else 0.0 + + return { + "url_length": url_length, + "dot_count": dot_count, + "hyphen_count": hyphen_count, + "at_symbol": at_symbol, + "has_ip": has_ip, + "digit_ratio": digit_ratio, + "subdomain_count": subdomain_count, + "path_depth": path_depth, + "has_https": has_https, + "domain_entropy": domain_entropy, + "suspicious_tld": suspicious_tld, + "has_port": has_port, + "query_param_count": query_param_count, + "fragment_present": fragment_present, + } diff --git a/sentinel_edge/privacy/__init__.py b/sentinel_edge/privacy/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..4e2207d2bacecc14b3f094ce475efde398b6c7b4 --- /dev/null +++ b/sentinel_edge/privacy/__init__.py @@ -0,0 +1 @@ +"""Differential privacy utilities.""" diff --git a/sentinel_edge/privacy/dp_noise.py b/sentinel_edge/privacy/dp_noise.py new file mode 100644 index 0000000000000000000000000000000000000000..1a4a7065bb7898bc1a42db6a4fe3653ff77d27df --- /dev/null +++ b/sentinel_edge/privacy/dp_noise.py @@ -0,0 +1,132 @@ +"""Differential privacy noise injection for federated learning. + +Implements the Gaussian mechanism for (epsilon, delta)-differential +privacy, applied to gradient deltas before they are sent from an +edge device to the central aggregator. +""" + +from __future__ import annotations + +import math + +import numpy as np + + +class DPNoiseInjector: + """Calibrated Gaussian noise injector for gradient privacy. + + Uses the analytic Gaussian mechanism: given a function with L2 + sensitivity *S*, adding Gaussian noise with + + sigma = S * sqrt(2 * ln(1.25 / delta)) / epsilon + + satisfies (epsilon, delta)-differential privacy. + + Parameters + ---------- + epsilon : float + Privacy budget. Smaller values yield stronger privacy but more + noise (and slower convergence). Typical range: 0.1 -- 1.0. + delta : float + Probability of privacy breach. Must be negligibly small + relative to the dataset size. + """ + + def __init__( + self, + epsilon: float = 0.3, + delta: float = 1e-5, + ) -> None: + if epsilon <= 0: + raise ValueError(f"epsilon must be positive, got {epsilon}") + if delta <= 0 or delta >= 1: + raise ValueError(f"delta must be in (0, 1), got {delta}") + self.epsilon = epsilon + self.delta = delta + + # ------------------------------------------------------------------ + # Public API + # ------------------------------------------------------------------ + + def compute_sigma(self, sensitivity: float) -> float: + """Compute the noise standard deviation for a given sensitivity. + + Parameters + ---------- + sensitivity : float + The L2 sensitivity of the query / gradient computation. + + Returns + ------- + float + Standard deviation (sigma) for the Gaussian noise. + """ + return ( + sensitivity + * math.sqrt(2.0 * math.log(1.25 / self.delta)) + / self.epsilon + ) + + def add_noise( + self, + gradient_delta: np.ndarray, + n_local_samples: int, + ) -> np.ndarray: + """Add calibrated Gaussian noise to a gradient delta vector. + + The L2 sensitivity is taken as ``1 / n_local_samples`` under the + assumption that per-sample gradient contributions have been + clipped to unit norm (see :meth:`clip_gradient`). + + Parameters + ---------- + gradient_delta : np.ndarray + The (clipped) gradient delta to be transmitted. + n_local_samples : int + Number of local training samples used to compute the delta. + Larger values reduce the sensitivity and therefore the noise. + + Returns + ------- + np.ndarray + Noisy gradient delta with the same shape and dtype as the + input. + """ + if n_local_samples <= 0: + raise ValueError( + f"n_local_samples must be positive, got {n_local_samples}" + ) + + sensitivity = 1.0 / n_local_samples + sigma = self.compute_sigma(sensitivity) + noise = np.random.normal( + loc=0.0, + scale=sigma, + size=gradient_delta.shape, + ) + return gradient_delta + noise.astype(gradient_delta.dtype) + + def clip_gradient( + self, + gradient: np.ndarray, + max_norm: float = 1.0, + ) -> np.ndarray: + """Clip a gradient vector to bounded L2 sensitivity. + + Parameters + ---------- + gradient : np.ndarray + Raw gradient vector (any shape). + max_norm : float + Maximum allowed L2 norm. Gradients exceeding this are + scaled down proportionally. + + Returns + ------- + np.ndarray + Gradient with ``||g||_2 <= max_norm``. + """ + grad_norm = float(np.linalg.norm(gradient)) + if grad_norm > max_norm and grad_norm > 0.0: + return gradient * (max_norm / grad_norm) + return gradient.copy() diff --git a/sentinel_edge/privacy/gradient_delta.py b/sentinel_edge/privacy/gradient_delta.py new file mode 100644 index 0000000000000000000000000000000000000000..2883784dee21e4f30e8156959c606132c4167b0a --- /dev/null +++ b/sentinel_edge/privacy/gradient_delta.py @@ -0,0 +1,69 @@ +"""Gradient delta computation for federated learning. + +Computes the difference between local model weights (trained on the +edge device) and the current global model weights, and applies +aggregated deltas back to the global model. These operations form +the core of the FedAvg / FedSGD communication protocol. +""" + +from __future__ import annotations + +import numpy as np + + +class GradientDeltaComputer: + """Stateless helper for computing and applying weight deltas. + + In the federated learning round-trip: + + 1. The edge device downloads global weights. + 2. Local fine-tuning produces updated local weights. + 3. ``compute_delta`` calculates the difference. + 4. The delta is (optionally) clipped and noised by ``DPNoiseInjector``. + 5. The noisy delta is sent to the central hub. + 6. The hub aggregates deltas from multiple devices. + 7. ``apply_delta`` updates the global model. + """ + + def compute_delta( + self, + local_weights: np.ndarray, + global_weights: np.ndarray, + ) -> np.ndarray: + """Compute the weight delta (local minus global). + + Parameters + ---------- + local_weights : np.ndarray + Weights after local training on the edge device. + global_weights : np.ndarray + Current global model weights. + + Returns + ------- + np.ndarray + ``local_weights - global_weights``, same shape and dtype. + """ + return local_weights - global_weights + + def apply_delta( + self, + global_weights: np.ndarray, + aggregated_delta: np.ndarray, + ) -> np.ndarray: + """Apply an aggregated delta to the global weights. + + Parameters + ---------- + global_weights : np.ndarray + Current global model weights. + aggregated_delta : np.ndarray + Mean (or weighted average) of per-device deltas received + by the hub. + + Returns + ------- + np.ndarray + Updated global weights. + """ + return global_weights + aggregated_delta