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🚀 Use Cases

Use Case	Description
🔐 Password strength scoring	Quantitative scoring (0–10) for any given password
🧠 Risk classification	Categorizes passwords as Safe, Weak, Common, Leaked, or Guessable
🕵️ Threat emulation	Emulates password cracking heuristics to spot vulnerable patterns
🧰 DevSecOps integration	Plug into CI/CD pipelines for password policy enforcement
👨‍💻 User awareness tools	Build frontend UX tools to give users feedback on password creation

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🔐 Trace.AI - AI-Powered Password Intelligence Engine

Trace.AI is an intelligent, ML-driven password checker designed to evaluate the strength, structure, and policy compliance of passwords. Built for modern security infrastructures, it leverages machine learning to identify weak, predictable, or non-compliant passwords based on real-world patterns and security datasets.

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🚀 Core Capabilities

✅ Password Strength Classification

Trace.AI scores passwords as Weak, Moderate, or Strong using a combination of rule-based feature extraction and machine learning.

🎯 Pattern Recognition

Detects predictable and insecure patterns such as:

• Keyboard walks (qwerty, asdf123)
• Common substitutions (p@ssw0rd)
• Repeated sequences (abcabc, 123123)
• Known dictionary or breached password similarities

📏 Policy Compliance

Checks if passwords meet enterprise-grade security policies, including: - Minimum length and entropy - Required character types (upper/lowercase, digit, special) - No whitespace, dictionary words, or reuse

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📊 Datasets Used

Trace.AI was trained using curated, high-quality password datasets:

Dataset	Description	Purpose
RockYou (Filtered)	Real-world leaked passwords, filtered for quality	Weak/Breached training data
Have I Been Pwned (HIBP)	613M+ SHA-1 leaked passwords	Breach detection, negative samples
zxcvbn Dataset	Rule-based scoring framework by Dropbox	Strength benchmarking and feature design

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🧠 Machine Learning Models

Trace.AI supports and evaluates multiple ML models for robustness:

Model	Strengths	Use
RandomForest	Non-linear classification, interpretable, fast	Production baseline
XGBoost	Handles imbalance, high accuracy, fast inference	Advanced detection
Logistic Regression	Lightweight, interpretable	Edge device / fallback model

All models are trained using engineered features like: - Length, character diversity - Entropy - Keyboard patterns - Regex-based leetspeak and substitution scoring

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Project Goals

Trace.AI is engineered to support the following goals:

Feature	Description
🔐 Password Strength Estimator	Predict if password is Weak, Moderate, or Strong
🧠 Pattern Analyzer	Identify insecure sequences, leetspeak, keyboard walks
📜 Policy Validator	Check adherence to defined password policies
🧾 Breach Cross-check	Compare against breached datasets (HIBP)
📤 Exportable Reports	Download prediction logs for security audits
📈 Visual Dashboard	UI-based analysis of strength and structure (via Gradio)

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