AI-powered phishing URL detection using Random Forest ML, domain analysis, and live threat intelligence.
- 🤖 Random Forest ML model (20+ features)
- 🧬 Brand similarity detection (typosquatting)
- 🌐 OpenPhish threat intelligence
- 📊 Real-time dashboard with live scan log
- 📷 QR code scanner
- 🔒 SSL/HTTPS checks + entropy analysis
- Backend: Python, Flask, Scikit-learn
- Frontend: HTML, CSS, JavaScript
- ML: Random Forest Classifier
- Deployment: Render.com
cd backend
pip install -r requirements.txtpython train_model.pypython app.pyhttp://127.0.0.1:5000
- Push this repo to GitHub
- Go to Render Dashboard
- Click New → Web Service
- Connect your GitHub repo
- Render will auto-detect
render.yamland deploy
- Go to Render Dashboard
- Click New → Web Service
- Connect your repo or upload files
- Settings:
- Root Directory:
backend - Build Command:
pip install -r requirements.txt && python train_model.py - Start Command:
gunicorn app:app - Python Version: 3.11
- Root Directory:
Your live URL will be: https://phishguard.onrender.com
Analyze a URL for phishing threats.
{
"url": "http://secure-paypa1-login.com"
}Get recent scan log (real ML results).
Check backend status and model load state.
phisgaurd/
├── backend/
│ ├── app.py # Flask API
│ ├── model.py # Feature extraction
│ ├── train_model.py # Model training
│ ├── requirements.txt
│ └── phishing_model.pkl # Trained model
├── frontend/
│ ├── index.html # Homepage
│ ├── scanner.html # URL scanner
│ ├── dashboard.html # Threat dashboard
│ ├── about.html # Project info
│ ├── css/style.css
│ └── js/main.js
└── render.yaml # Render config
MIT