Catch-Phis

Overview

This project is a phishing detection system designed to identify and flag potential phishing attempts using a Django-based backend. The system leverages machine learning algorithms and various heuristics to analyze URLs for signs of phishing.

Features

• URL Analysis: Detect phishing attempts by analyzing URL structures and features.
• Machine Learning Integration: Utilize trained models (SVC/XGBOOST) for phishing detection.
• User Dashboard: Provide an interface for users to view website interaction stats.
• Admin Interface: Manage and configure the system from a Django-powered admin panel.
• Corporate Account Management: Enable the creation of corporate accounts for centralized tracking and management of employee phishing detection statistics.
• Access Control List Management: Allow manual addition of domains to the Access Control List, enabling users to classify domains as either whitelisted or blacklisted.
• Verified Domains Repository: Maintain a monthly updated repository of verified domains to ensure an accurate and current list of legitimate domains.
• Payment Integration: Integrate Stripe payment gateway for corporate subscription management.
• Browser Extension: Deploy a Chromium-based browser extension for real-time phishing detection and prevention.

Technologies Used

• Django: For the backend framework.
• Python: Programming language used for development.
• Scikit-learn / xgboost : For machine learning algorithms.
• PostgreSQL: Database for storing user data and analysis results.
• React js: JavaScript library for building dynamic user interfaces.
• Git/Github: Version control and repository management.
• Rabbit Mq: Message broker for task communication.
• Celery: For asynchronous task processing.
• Redis: In-memory data structure store for caching.
• Nginx: Reverse proxy server for handling requests.
• Docker: Containerization for consistent development environments.

Installation

Steps

1. Clone the repository

git clone https://github.com/SagunDevkota/Catch-Phis.git
cd Catch-Phis

2. Create a .container.env file inside the Catch-Phis/backend folder. This file will store environment variables for the containerized services:

touch backend/.container.env

DJANGO_SETTINGS_MODULE=

POSTGRES_HOST=
POSTGRES_DB=
POSTGRES_USER=
POSTGRES_PASSWORD=


RABBITMQ_DEFAULT_USER=
RABBITMQ_DEFAULT_PASS=

3. Create a .env file inside the Catch-Phis/backend/appdirectory. This file will store environment variables for the Django application:

touch backend/app/.env

DJANGO_DEBUG=
SECRET_KEY=

CELERY_BROKER=
CELERY_BACKEND=

host=
email=
password=

STRIPE_SECRET_KEY=
STRIPE_WEBHOOK_SECRET=

FINISH_URL=

4. Start the docker compose

docker-compose up

Configuration

Update the base/production/test.py file for any configuration changes if required:

Usage

1. Access the Application

Open your web browser and navigate to http://127.0.0.1/ to interact with the phishing detection system.

2. Admin Panel

Access the Django admin panel at http://127.0.0.1/admin/ to manage users, view logs, and configure the system.

3. API Endpoints Open http://127.0.0.1/api/docs/ for api documentation.

4. Database Debugging Open http://127.0.0.1/silk/ for database request (for test version).

5. Static Files Open http://127.0.0.1/static/ to serve django static Files

Contributing

If you'd like to contribute to this project, please fork the repository and submit a pull request. For major changes, please open an issue first to discuss what you would like to change.

License

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

Contact For any inquiries or issues, please contact Email