🛡️ PhishGuard - AI-Powered Phishing Protection

PhishGuard is a comprehensive phishing detection system that uses Machine Learning to identify malicious URLs in real-time. It includes a Web Dashboard, a REST API, and a Chrome Extension for active browsing protection.

✨ Features

• Real-Time Analysis: Scans URLs instantly using a trained XGBoost model.
• Chrome Extension: Protects you while you browse by showing warning banners on suspicious sites.
• Web Dashboard: Manually check URLs via a clean web interface.
• High Accuracy: Trained on over 65,000 verified phishing and benign URLs.

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🚀 Getting Started

Prerequisites

• Python 3.8+
• Node.js & npm (for the web dashboard)

1. Backend Setup (API)

The API handles the machine learning inference.

1. Install Python dependencies:

   pip install flask flask-cors pandas joblib scikit-learn xgboost

2. Navigate to the backend directory and start the API server:

   cd backend
   python api.py

   The API will start on http://127.0.0.1:5000. Keep this terminal open.

2. Web Dashboard Setup

The dashboard allows you to test URLs manually.

1. Install Node.js dependencies:

   npm install

2. Start the web server:

   npm run serve-web

   The dashboard will open at http://localhost:5500.

3. Chrome Extension Setup

Enable real-time protection in your browser.

1. Open Chrome and navigate to chrome://extensions/.
2. Toggle Developer mode (top right).
3. Click Load unpacked.
4. Select the extension folder located in this repository.
5. The PhishGuard icon should appear in your toolbar.

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🧠 Machine Learning Model

The system uses an XGBoost Classifier trained on linguistic and structural features of URLs (e.g., length, special characters, IP usage).

• Training: To retrain the model with the provided dataset:

  cd backend
  python train_model.py

  This will generate a new phishing_detector_model.pkl file.

• Prediction (CLI): To test a URL from the command line:

  cd backend
  python predict.py "http://suspect-url.com"

📁 Project Structure

• backend/: Contains API server, ML models, and database.
  • api.py: Flask API server.
  • phishing_detector_model.pkl: Trained XGBoost model.
  • users.db: SQLite database for user data.
• frontend/: Contains the web dashboard (HTML/CSS/JS).
  • index.html: Main landing page.
  • url_detection.html, qr_detection.html: Specialized tool pages.
• extension/: Source code for the Chrome Extension (manifest, scripts, popup).

⚠️ Note

This project is for educational and research purposes. While highly accurate, no anti-phishing tool is 100% perfect. Always verify URLs carefully.