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PHISHSHIELD 🛡️: Your Shield Against Phishing Website

PHISHSHIELD is a web application built to help you identify and avoid phishing websites. It leverages the power of machine learning, specifically Python and its libraries, to analyze website characteristics and predict their legitimacy. Stay safe online with PHISHSHIELD!

Technologies Used

  • Front-End: HTML, CSS, JavaScript
  • Back-End: Python (Flask framework)
  • Data Analysis: Pandas (Python library)
  • Datasets: phishing.csv, legitimateurls.csv (provided within the repository)

Screenshots

Website Homepage Website in Action

Getting Started

  1. **Clone the Repository:** 
    git clone https://github.com/your-username/PHISHSHIELD.git
  2. **Install Dependencies:** 
    cd PHISHSHIELD
pip install -r requirements.txt
  3. **Run the Application:** 
    python app.py
    Open your web browser and visit `http://127.0.0.1:5000/` (or `localhost:5000/` if applicable) to access the PHISHSHIELD web interface.

Data Files

  • `legitimateurls.csv`: Contains URLs of legitimate websites used for training the model.
  • `phishing.csv`: Contains URLs of phishing websites used for training the model.
  • `phishurls.csv` (Optional): If provided, this file might contain additional unknown URLs for PHISHSHIELD to analyze.

Additional Notes

  • The `pycache_` directory is automatically generated by Python and can be ignored.
  • The `static` directory contains static assets like images and JavaScript files used by the application.
  • The `templates` directory contains HTML templates for the web interface.
  • The `Procfile` is used for deployment purposes (optional).

Future Enhancements

  • Implement a user interface for entering suspicious URLs.
  • Integrate the model with a browser extension for real-time phishing detection.
  • Explore more advanced machine learning techniques for improved accuracy.

Contributing

We welcome contributions to improve PHISHSHIELD. Feel free to fork the repository, make changes, and submit pull requests.

Note: Due to security concerns, using the private link directly in the HTML is not recommended. Consider using a Content Delivery Network (CDN) to host the animated GIF and reference it from there.

Thesis on Python's Role (Optional):

PHISHSHIELD effectively utilizes Python for several reasons:

  • Powerful Machine Learning Libraries: Scikit-learn (popular for ML) and TensorFlow (deep learning) can be used for analysis and prediction.
  • Data Analysis Capabilities: Pandas offers efficient data manipulation and exploration for training and evaluation.
  • Web Development: Flask provides a lightweight and versatile framework for building the backend of PHISHSHIELD.

Security Considerations:

  • CDN: Employ a CDN to host the background image for better performance and security.
  • **

Contributing 🤝

We welcome contributions to improve PHISHSHIELD. Feel free to fork the repository, make changes, and submit pull requests.