--- license: apache-2.0 title: EmailGuard2 sdk: gradio emoji: 🌍 colorFrom: blue colorTo: pink short_description: The only secure and rational email phishing detector --- # EmailGuard2 : Advanced Phishing Detection System A multi-model ensemble system for detecting phishing attempts in emails, URLs, and text messages using AI and feature engineering. ## Features - Multi-model ensemble prediction - Advanced feature extraction and analysis - Real-time phishing detection - Web-based user interface - Risk scoring and confidence reporting - URL and email content analysis ## Installation 1. Clone the repository: ```bash git clone cd emailguard-phishing-detection ``` 2. Install dependencies: ```bash pip install -r requirements.txt ``` 3. Run the application: ```bash python app.py ``` 4. Open your browser and go to `http://localhost:7860` ## Usage 1. Enter email content, URL, or suspicious text in the input field 2. Click "Advanced Analysis" to process the input 3. Review the results including risk level and confidence scores ## Models Used - Primary: `cybersectony/phishing-email-detection-distilbert_v2.4.1` - URL Specialist: Custom URL analysis model - Feature Engine: Hand-crafted pattern detection rules ## Detection Features ### URL Analysis - Suspicious domain detection - Shortened URL identification - Malicious link patterns ### Content Analysis - Urgency keyword detection - Money-related terms - Personal information requests - Spelling error patterns - Excessive capitalization ### Risk Assessment - HIGH RISK: Strong phishing indicators (>60% confidence) - MEDIUM RISK: Suspicious patterns (30-60% confidence) - LOW RISK: Appears legitimate (<30% confidence) ## System Requirements - Python 3.8+ - 4GB+ RAM - Internet connection (for initial model download) ## Technical Details The system uses: - PyTorch for deep learning models - Transformers for NLP processing - Gradio for web interface - Custom ensemble voting mechanism - Feature-based risk adjustment ## Example Inputs **Phishing Example:** ``` URGENT: Your PayPal account has been limited! Verify immediately at http://paypal-security-check.suspicious.com/verify ``` **Legitimate Example:** ``` Hi Sarah, Thanks for the quarterly report. Let's discuss in tomorrow's meeting. Best, Mike ``` ## Configuration Model configuration in `app.py`: ```python MODELS = { "primary": "cybersectony/phishing-email-detection-distilbert_v2.4.1", "url_specialist": "cybersectony/phishing-email-detection-distilbert_v2.4.1" } ``` ## Limitations - This is an educational/research tool - Always verify suspicious content through official channels - May produce false positives/negatives - Requires manual verification for critical decisions ## License Apache2.0 License ## Contributing 1. Fork the repository 2. Create a feature branch 3. Make your changes 4. Submit a pull request ## Support For issues and questions, please use the GitHub issue tracker.