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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 <repository-url> | |
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. |