Suricata AI Analysis

Suricata AI analysis

Suricata AI Analysis — Intelligent Detection of Anomalies and Network Threats

Suri Oculus enhances Suricata with an AI-driven traffic analysis module capable of detecting anomalies, identifying behavioral deviations, and classifying suspicious network activity. The model operates in real time and is optimized for low-power hardware.

Behavior-Based Detection Using Machine Learning

The AI module uses Isolation Forest models trained on network flow, TLS, HTTP, DNS, and meta-traffic features. Instead of relying solely on static signatures, it identifies abnormal patterns that deviate from normal behavior.

Capabilities:

  • anomaly scoring

  • deviation tagging

  • flow classification

  • behavior clustering

  • low-latency inference

This approach significantly improves detection accuracy for unknown threats.

C++ Feature Extraction for Maximum Performance

To ensure minimal overhead, Suri Oculus uses a high-performance C++ feature extraction engine.
It processes Suricata events and JSON logs in real time:

  • flow statistics

  • timing profiles

  • JA3 / JA3S fingerprints

  • packet entropy

  • DNS query behavior

  • TLS handshake metadata

The output is passed to the Python ML layer via FastAPI.

Real-Time Integration With the Suricata Dashboard

Suricata AI Analysis integrates directly into the dashboard:

  • anomaly counters

  • real-time charts

  • detailed anomaly logs

  • per-protocol analysis (Flow, TLS, HTTP, DNS)

  • manual data input for testing

This allows analysts to correlate anomalies with alerts and flow events instantly.

Works Even on Low-Power Hardware

The AI module is optimized for:

  • budget routers

  • home labs

  • microservers

  • ARM SBCs

  • cloud micro-instances

The combined C++/Python architecture ensures stable operation with minimal CPU use.

Related Modules

Explore other modules:

External Resources

Learn more about Suricata and anomaly detection:
– https://suricata.io/documentation/
– https://oisf.net/