README.md

metadata

title: Phishing Detector
emoji: 🔍
colorFrom: red
colorTo: blue
sdk: gradio
sdk_version: 4.39.0
app_file: app.py
pinned: false

Phishing Detector

A comprehensive multi-model phishing detection system using:

🤖 Models

• DeBERTa + LSTM: Advanced transformer with attention mechanism (khoa-done/phishing-detector)
• BERT: Fine-tuned BERT model (th1enq/bert_checkpoint)
• XGBoost: Traditional ML with feature engineering (th1enq/xgboost_checkpoint)

✨ Features

• URL Structure Analysis: Extract 30+ features from URL patterns
• HTML Content Analysis: Extract 43+ features from webpage content
• Combined Predictions: Weighted ensemble of all models
• Visual Attention Weights: See which tokens influence decisions
• Real-time Web Scraping: Fetch and analyze live websites
• Multi-tab Interface: Compare results across different models

🚀 Usage

1. Enter a URL: System will fetch the webpage and analyze both URL structure and content
2. Enter text: Direct analysis of suspicious text content
3. Compare Models: Use different tabs to see how each model performs

📊 Model Performance

• DeBERTa + LSTM: Best for context understanding with attention visualization
• BERT: Reliable baseline with robust predictions
• XGBoost: Fast traditional ML approach with feature interpretability

🔧 Technical Details

• All models loaded from Hugging Face Hub for easy deployment
• Feature extraction modules included for XGBoost functionality
• Dark theme optimized interface with visual analytics
• Graceful fallbacks if models fail to load

📝 Examples

Try these URLs to see the system in action:

• https://github.com/user/repo (should be benign)
• http://suspicious-phishing-site.example (simulated phishing)
• Or paste any suspicious email content for analysis