detecting-anomalies-in-industrial-control-systems

# Detecting Anomalies in Industrial Control Systems ## When to Use - When deploying continuous monitoring for OT environments that lack intrusion detection - When building behavior-based detection to complement signature-based IDS in OT networks - When establishing baselines for deterministic SCADA communications to detect deviations - When integrating machine learning anomaly detection with OT security monitoring platforms - When investigating alerts from Nozomi Guardian or Dragos Platform that require deeper analysis **Do not use** for signature-based detection of known exploits (see detecting-attacks-on-scada-systems), for IT network anomaly detection without OT protocols, or as a replacement for process safety systems (SIS). ## Prerequisites - Passive network monitoring sensors on OT network SPAN/TAP ports - Minimum 2-4 weeks of baseline traffic capture during normal operations - Python 3.9+ with scikit-learn, numpy, pandas for ML model training - Process historian access for physical process correlation data - Understanding of normal operational patterns including shift changes, batch processes, and maintenance windows ## Workflow ### Step 1: Build Multi-Dimensional Baseline Model Capture and model the deterministic behavior of ICS communications across multiple dimensions: timing, protocol behavior, and network topology. ```python #!/usr/bin/env python3 """ICS Anomaly Detection System. Builds multi-dimensional baselines from OT network traffic and detects anom...