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🛡️ Real-Time Ransomware Detection System

Welcome to the Real-Time Ransomware Detection System! This project is designed to provide a comprehensive solution for detecting ransomware attacks in real-time. By monitoring system activities and analyzing potential threats, the system aims to identify and respond to ransomware behaviors promptly, helping to safeguard valuable data and maintain system integrity.

✨ Features

  • 🚨 Real-Time Detection: Monitors system activities to detect ransomware attacks as they happen.
  • 🔍 Behavior Analysis: Analyzes file and process behaviors to identify suspicious activities.
  • 🛡️ Alert System: Sends notifications when ransomware activities are detected.
  • 📊 Dashboard: Provides a user-friendly interface for monitoring system status and threat alerts.
  • 🛠️ Customizable Rules: Allows users to define and adjust detection rules based on their specific needs.

🛠️ Technology Stack

Backend:

  • 🐍 Python
  • 🛠️ Flask (for handling API requests)
  • 📊 Pandas (for data analysis)
  • 📈 Scikit-learn (for machine learning algorithms)

Frontend:

  • ⚛️ React.js
  • 🎨 CSS (for styling)

Database:

  • 🗄️ SQLite

🚀 Getting Started

Follow these steps to set up the project locally.

Prerequisites

  • Python 3.8+
  • Node.js 14+
  • npm (or yarn)

Installation

  1. Clone the repository:

    git clone https://github.com/Apatoma/Real-Time-Ransomware-Detection-System.git
cd Real-Time-Ransomware-Detection-System

  2. Set up the backend:

    cd backend
pip install -r requirements.txt
python run.py

    The backend server will start on http://localhost:5000.

  3. Set up the frontend:

    cd frontend
npm install
npm start

    The frontend will be accessible at http://localhost:3000.

  4. Access the application:

📝 Usage

  • Monitor System Activities: Use the dashboard to observe real-time system activity and detect potential ransomware threats.
  • Review Alerts: Check for alerts and notifications when suspicious activities are detected.
  • Customize Detection Rules: Adjust detection parameters and rules to tailor the system to your specific environment and threat landscape.
  • Analyze Behavior: View detailed analyses of detected behaviors to understand the nature and potential impact of threats.

🛤️ Future Enhancements

  • 🤖 Advanced Detection Algorithms: Integrate more sophisticated machine learning models for improved detection accuracy.
  • 📈 Enhanced Analytics: Develop more comprehensive analytics and reporting features.
  • 🌐 Cloud Integration: Extend the system to support cloud-based monitoring and detection.
  • 🔒 Incident Response Integration: Add features for automated incident response and remediation.

🧑‍💻 Contributing

Contributions are welcome! Please fork the repository, make your changes, and submit a pull request.

📄 License

This project is licensed under the MIT License. See the LICENSE file for more details.

Made with ❤️ by Alejandro