🛡️ Phishing Guard v2.0

Final Year IEEE Project | AI-Powered Phishing Detection System

AI-powered phishing detection with 93 ML features, IDN protection, and enterprise security.

🚀 Quick Start

Prerequisites

# Install Python dependencies
pip install -r requirements.txt

Run Detection

# CLI mode
python 04_inference/service.py https://example.com

# API server
python 04_inference/api.py

# Run tests
pytest tests/

📦 Project Structure

phishing_detection_project/
├── 01_data/              # Datasets & TLDs
├── 02_models/            # ML models (joblib)
├── 03_training/          # Training scripts + MLflow
├── 04_inference/         # API + Service
├── 05_utils/             # 93 feature extractors
├── 06_notebooks/         # Jupyter notebooks
├── 07_configs/           # Configuration files
├── browser-extension/    # Chrome/Firefox extension
├── tests/                # Test suite
│
├── Dockerfile            # Container deployment
├── docker-compose.yml    # Docker orchestration
├── requirements.txt      # Python dependencies
└── README.md             # This file

🎯 Features

• 93 ML Features - Advanced feature engineering
• 4-Category Classification - Legitimate, Phishing, AI-Generated, Phishing Kit
• IDN Protection - Unicode homograph attack detection
• Browser Extension - Real-time link protection
• MLflow Integration - Model versioning & experiment tracking
• API Server - RESTful endpoints with JWT authentication

🧪 Testing

# Run test suite
pytest tests/ -v

# Run specific tests
python -m pytest tests/test_security.py
python -m pytest tests/test_comprehensive.py

📊 Detection Accuracy

Metric	Value
F1 Score	99.82%
Precision	99.81%
Recall	99.83%
Features	93
Classification	4 categories

🔐 Security Features

• JWT Authentication (24hr tokens)
• Rate Limiting (100 req/min)
• SSRF Protection
• TLS 1.3 Enforcement
• Input Validation

🏗️ Architecture

┌─────────────────────────────────────────┐
│         User Interfaces                 │
│    CLI • API • Browser Extension        │
└─────────────────┬───────────────────────┘
                  │
        ┌────────┴────────┐
        │ Detection Core │
        │  93 Features   │
        └────────┬────────┘
                  │
    ┌─────────────┼─────────────┐
    │             │             │
┌───┴───┐   ┌────┴────┐   ┌────┴────┐
│ Tier 1│   │ Tier 2  │   │ Tier 3  │
│ ML    │   │ Typosquat│   │ MLLM   │
└───────┘   └─────────┘   └─────────┘

🤖 ML Models

• Random Forest Classifier (200 estimators)
• XGBoost Classifier (50 estimators)
• Ensemble Method: Soft Voting
• Optional: Qwen2.5-3B for AI-generated phishing detection

🌐 Browser Extension

See browser-extension/ for installation instructions.

cd browser-extension
npm install
npm run build

📚 Documentation

• API Documentation - API endpoints
• Security Policy - Security details
• Deployment Guide - Docker deployment
• Browser Extension - Extension setup

📞 Support

• Author: Akarsh Bandi
• Email: akarshbandi82@gmail.com
• GitHub: BandiAkarsh

📄 License

MIT License - See LICENSE file

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🎓 Final Year IEEE Project