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Detecting Anomalies In Industrial Control Systems
This skill covers deploying anomaly detection systems for industrial control environments using machine learning models trained on OT network baselines, physics-based process models, and behavioral analysis of industrial protocol communications. It addresses building normal behavior profiles for SCADA polling patterns, detecting deviations in Modbus/DNP3/OPC UA traffic, identifying rogue devices, and correlating network anomalies with physical process data from historians.
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# 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 anomalies using statistical and machine learning methods.
Designed for deterministic SCADA communication patterns.
"""
import json
import sys
import time
import warnings
from collections import defaultdict
from datetime import datetime, timedelta
from dataclasses import dataclass, field
import numpy as np
import pandas as pd
from sklearn.ensemble import IsolationForest
from sklearn.preprocessing import StandardScaler
warnings.filterwarnings("ignore")
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