ML-Enhanced Suricata IDS/IPS for Network Security
Next-Generation Firewall Security with AI-Powered Threat Detection.
Introduction
- Intrusion Detection Systems (IDS) and Intrusion Prevention Systems (IPS) are essential for identifying and preventing unauthorized access to network systems.
- Suricata is a high-performance, open-source IDS/IPS engine that detects network anomalies using predefined rules.
- Traditional rule-based systems like Suricata are prone to high false positives and may struggle with evolving cyber threats and zero-day attacks.
- Long Short-Term Memory (LSTM) networks, a type of Recurrent Neural Network (RNN), excel at recognizing patterns in sequential data, making them suitable for detecting complex, time-dependent network threats.
- This project proposes integrating LSTM with Suricata to enhance the accuracy of intrusion detection, reduce false positives, and detect sophisticated or evolving attacks in real-time.
- The goal is to leverage LSTM’s learning capabilities to complement Suricata’s rule-based approach, resulting in a hybrid system that improves both detection efficiency and scalability.
Key Features
- ML-Based Anomaly Detection: Real-time improved threat detection.
- Adaptive Threat Response: Dynamic rule adjustment based on ML predictions.
- Predictive Network Analytics: Preemptively block suspicious activity.
- Automated Responses: Autonomous log analysis and alert flagging.
- Visualizations: Graphs showing improved detection rates.
Live Demo
See our ML-enhanced Suricata detecting suspicious activities in real-time.
Performance Comparison
Meet the Team
Md. Ibrahim
Team Leader
Priyadarshan
Cyber Security Specialist
Sarvesh
Model Trainer
Shubham Pandey
Web Developer & Git
Documentation & Resources
Check out the GitHub Repository for the full project documentation and code samples.