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license: apache-2.0
language:
- en
metrics:
- accuracy
- precision
- f1
library_name: sklearn
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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 `Weak`, `Fairly Strong`, `Strong` |
| 🕵️ 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**, **Fairly Strong**, 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 |
|--------|-------------|
| **cleanpasswordlist(modified)** | Real-world passwords list, modified and feature engineered for better prediction and scoring |
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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 |
| **Decision Trees** | 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 |
| 📤 Exportable Reports | Download prediction logs for security audits |
| 📈 Visual Dashboard | UI-based analysis of strength and structure (via Gradio) |
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