Gmail Phishing Detector

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A machine learning-based system to detect phishing emails directly within Gmail. This project consists of a Python backend API and a Chrome Extension that scans emails in real-time and alerts users automatically.

Features

• 🛡️ Real-time phishing detection inside Gmail
• 🤖 Machine learning models trained with Random Forest and SGD classifiers
• 🚀 RESTful Flask backend for predictions
• 🌐 Chrome Extension with content and background scripts
• 📊 Data preprocessing and feature extraction pipeline
• ⚡ Fast response times for email scanning

Tech Stack

• Python 3.10+
• Flask - RESTful API framework
• Scikit-learn - Machine learning library
• JavaScript - Chrome Extension
• Random Forest & SGD - ML algorithms

Project Structure

Phishing-detection/
├── BackendRest.py              # Flask API server
├── TeachMLM.py                 # ML model training script
├── Preproccess_data.py         # Data cleaning & vectorization
├── manifest.json               # Chrome extension manifest
├── background.js               # Extension background script
├── content.js                  # Extension content script
├── data/                       # Preprocessed datasets
├── models/                     # Trained ML models
├── requirements.txt            # Python dependencies
├── .env.example                # Environment variables template
└── LICENSE                     # MIT License

Prerequisites

• Python 3.10 or later
• Chrome browser
• Git
• pip (Python package manager)

Installation

1. Clone the Repository

git clone https://github.com/the3y3-code/Phishing-detection.git
cd Phishing-detection

2. Install Python Dependencies

pip install -r requirements.txt

3. Configure Environment Variables (Optional)

Copy the example environment file and customize if needed:

cp .env.example .env

Usage

Train Models (Optional)

If you want to retrain the models with your own dataset:

1. Place your dataset CSV file (e.g., Phishing_validation_emails.csv) in the project root.
2. Run preprocessing:

python Preproccess_data.py

3. Train the models:

python TeachMLM.py

Run the Backend Server

Start the Flask API server:

python BackendRest.py

The server will be accessible at http://localhost:5000.

Install Chrome Extension

1. Open Chrome and navigate to chrome://extensions/
2. Enable Developer mode (toggle in top right)
3. Click Load unpacked
4. Select the Phishing-detection project directory

Using the Extension

1. Ensure the backend server is running
2. Open Gmail in your browser
3. The extension will automatically scan incoming emails
4. Alerts will appear if phishing attempts are detected

Running Tests

To run tests (if implemented):

pytest tests/

API Endpoints

POST /predict

Predicts if an email is phishing or legitimate.

Request:

{
  "email_content": "Your email text here"
}

Response:

{
  "prediction": "phishing" | "legitimate",
  "confidence": 0.95
}

Contributing

Contributions are welcome! Please follow these steps:

1. Fork the repository
2. Create a feature branch (git checkout -b feature/AmazingFeature)
3. Commit your changes (git commit -m 'Add some AmazingFeature')
4. Push to the branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

Please read CONTRIBUTING.md for details on our code of conduct and development process.

Roadmap

• Add deep learning models (LSTM, BERT)
• Support for multiple email providers
• Mobile app version
• Real-time threat intelligence integration
• Multi-language support

License

This project is licensed under the MIT License - see the LICENSE file for details.

Acknowledgments

• Scikit-learn community for ML tools
• Flask framework contributors
• Chrome Extensions documentation

Contact

the3y3-code - GitHub Profile

Project Link: https://github.com/the3y3-code/Phishing-detection

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