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Botnet Detection System

This Python script provides a sophisticated botnet detection system that leverages signature-based detection, machine learning algorithms, behavioral analysis, and traffic profiling to identify potential botnet activity in real-time. It also includes advanced alerting capabilities and integration with IP reputation services and SIEM for enhanced threat detection and centralized monitoring.

Features

• Signature-based detection: Detects botnet traffic based on dynamically updated signatures.
• Machine learning integration: Utilizes machine learning algorithms to improve detection accuracy and identify evolving patterns of botnet traffic.
• Behavioral analysis: Implements behavioral analysis techniques to identify suspicious behavior beyond signature-based detection.
• Traffic profiling: Develops a traffic profiling system to establish a baseline of normal network behavior and detect anomalies.
• IP reputation services integration: Integrates with IP reputation services to assess the reputation of IP addresses and block traffic from known malicious sources.
• Advanced alerting: Enhances email alerts with detailed information, including severity levels, packet analysis summaries, and recommended actions.
• SIEM integration: Integrates with a Security Information and Event Management (SIEM) system for centralized monitoring and better incident response capabilities.
• Multi-threaded processing: Optimizes packet processing by performing real-time analysis in a separate thread to handle large volumes of traffic more efficiently.
• Traffic visualization: Visualizes traffic profiling using matplotlib to provide insights into network activity, making it easier to identify patterns and anomalies visually.
• Dynamic signature updates: Periodically updates botnet signatures from an external source to ensure the detection system remains up-to-date with the latest threats.

Dependencies

• Python 3.x
• Scapy
• Matplotlib (for traffic visualization)

Usage

1. Ensure Python 3.x, Scapy, and Matplotlib are installed on your system.
2. Run the script botnet_detection.py.
3. Monitor the output for detected botnet activity and alerts.

Configuration

• Modify the botnet signatures dynamically by implementing a mechanism to update signatures from external sources or databases.
• Configure machine learning models and behavioral analysis techniques as per requirements.
• Adjust the traffic profiling system parameters to fine-tune anomaly detection.

License

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

Acknowledgments

• This script was developed for educational and research purposes to demonstrate advanced botnet detection techniques.
• Special thanks to the contributors and the Scapy development team for their valuable contributions.

CONTRIBUTORS WELCOME! HELP US MAKE THIS BOTNET DETECTION SYSTEM EVEN MORE EFFECTIVE AND ROBUST.

If you find this project useful or interesting, please leave a star ⭐ to support further development to make this script more sophisticated and worthwhile.....