🛡️ 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:

   • The backend API will be running on http://localhost:5000.
   • The frontend application will be available at http://localhost:3000.

📝 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.

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Made with ❤️ by Alejandro