| |
|
|
| import os |
| import numpy as np |
| import pandas as pd |
| import tensorflow as tf |
| from tensorflow.keras.models import load_model |
| import pickle |
| from flask import Flask, request, jsonify, render_template |
|
|
| |
| MODEL_PATH = 'cicids2017_mlp_model.keras' |
| SCALER_PATH = 'cicids2017_scaler.pkl' |
| EXPECTED_FEATURES = 49 |
| app = Flask(__name__) |
|
|
| |
| loaded_model = None |
| loaded_scaler = None |
|
|
| def load_assets(): |
| """Load the model and scaler only once when the app starts.""" |
| global loaded_model, loaded_scaler |
| |
| |
| if not os.path.exists(MODEL_PATH) or not os.path.exists(SCALER_PATH): |
| print("🚨 ERROR: Model or scaler files not found. Ensure they are in the same directory.") |
| return False |
| |
| try: |
| |
| loaded_model = load_model(MODEL_PATH) |
| |
| |
| with open(SCALER_PATH, 'rb') as file: |
| loaded_scaler = pickle.load(file) |
| |
| print(f"✅ Assets loaded successfully. Model ready for {EXPECTED_FEATURES} features.") |
| return True |
| |
| except Exception as e: |
| print(f"🚨 FATAL ERROR loading assets: {e}") |
| return False |
|
|
| |
| load_assets() |
|
|
|
|
| |
| HTML_TEMPLATE = """ |
| <!doctype html> |
| <title>NIDS Prediction</title> |
| <style> |
| body {{ font-family: sans-serif; max-width: 800px; margin: auto; padding: 20px; }} |
| textarea {{ width: 100%; min-height: 150px; padding: 10px; box-sizing: border-box; }} |
| h1 {{ color: #007bff; }} |
| .result {{ padding: 15px; border-radius: 5px; margin-top: 20px; }} |
| .attack {{ background-color: #f8d7da; border: 1px solid #f5c6cb; color: #721c24; }} |
| .benign {{ background-color: #d4edda; border: 1px solid #c3e6cb; color: #155724; }} |
| .error {{ background-color: #ffeeba; border: 1px solid #ffc720; color: #664d03; }} |
| </style> |
| <h1>Network Intrusion Detection System (NIDS)</h1> |
| <p>Paste {num_features} comma-separated network flow features below for prediction.</p> |
| |
| <form method="POST" action="/"> |
| <textarea name="features" placeholder="e.g., 0.1, 10.5, 0.003, ...">{example_data}</textarea><br><br> |
| <input type="submit" value="Predict Traffic Type" style="padding: 10px 20px; background-color: #007bff; color: white; border: none; cursor: pointer;"> |
| </form> |
| |
| {result_html} |
| """ |
|
|
|
|
| |
|
|
| @app.route('/', methods=['GET', 'POST']) |
| def home(): |
| """Handles both the form display (GET) and prediction submission (POST).""" |
| result_html = "" |
| example_data = "" |
| |
| if request.method == 'POST': |
| try: |
| |
| features_str = request.form.get('features') |
| features_list = [float(x.strip()) for x in features_str.split(',') if x.strip()] |
| example_data = features_str |
| |
| if len(features_list) != EXPECTED_FEATURES: |
| raise ValueError(f"Input must contain exactly {EXPECTED_FEATURES} features, but received {len(features_list)}.") |
|
|
| |
| input_array = np.array(features_list).reshape(1, -1) |
| input_scaled = loaded_scaler.transform(input_array) |
| |
| |
| prediction_proba = loaded_model.predict(input_scaled, verbose=0)[0][0] |
| prediction_class = 1 if prediction_proba > 0.5 else 0 |
| |
| |
| class_label = "🚨 ATTACK" if prediction_class == 1 else "✅ BENIGN" |
| css_class = "attack" if prediction_class == 1 else "benign" |
| |
| result_html = f""" |
| <div class="result {css_class}"> |
| <h3>Prediction: {class_label}</h3> |
| <p>Probability of Attack (1): <b>{prediction_proba:.5f}</b></p> |
| </div> |
| """ |
|
|
| except ValueError as e: |
| result_html = f""" |
| <div class="result error"> |
| <h3>Input Error</h3> |
| <p>{e}</p> |
| </div> |
| """ |
| except Exception as e: |
| result_html = f""" |
| <div class="result error"> |
| <h3>Prediction Error</h3> |
| <p>An unexpected error occurred: {e}</p> |
| </div> |
| """ |
|
|
| return render_template_string( |
| HTML_TEMPLATE, |
| num_features=EXPECTED_FEATURES, |
| example_data=example_data, |
| result_html=result_html |
| ) |
|
|
| if __name__ == '__main__': |
| |
| app.run(host='0.0.0.0', port=5000) |
