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.