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https://github.com/vvv-keys/keys-custom-idps
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 DETECTION!
https://github.com/vvv-keys/keys-custom-idps
botnet botnet-detection botnet-tool botnet-tools botnets cybersecurity cybersecurity-tools idps idpshook python python-3 python-script python3 safety-monitoring security security-audit security-automation security-tools
Last synced: about 2 months ago
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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 DETECTION!
- Host: GitHub
- URL: https://github.com/vvv-keys/keys-custom-idps
- Owner: vVv-Keys
- Created: 2023-09-12T07:22:15.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2024-07-14T00:15:40.000Z (6 months ago)
- Last Synced: 2024-07-14T01:26:04.411Z (6 months ago)
- Topics: botnet, botnet-detection, botnet-tool, botnet-tools, botnets, cybersecurity, cybersecurity-tools, idps, idpshook, python, python-3, python-script, python3, safety-monitoring, security, security-audit, security-automation, security-tools
- Language: Python
- Homepage:
- Size: 67.4 KB
- Stars: 1
- Watchers: 1
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
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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](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.....