Detecting Anomalies In Industrial Control Systems

Detecting Anomalies In Industrial Control Systems

Overview

Cybersecurity skill for detecting anomalies in industrial control systems. Follows industry best practices and security standards.

When to Use

Trigger phrases:

• "detecting anomalies in industrial control systems"

• "This skill covers deploying anomaly detection systems for industrial control env"

• 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).

When NOT to Use

• When you lack proper authorization for testing
• For production systems without change management
• When the task requires legal or compliance expertise beyond technical scope

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

# Example: IOC detection
import re

IOC_PATTERNS = {
    "ip": r"\b(?:\d{1,3}\.){3}\d{1,3}\b",
    "domain": r"\b[a-z0-9-]+\.[a-z]{2,}\b",
    "hash_md5": r"\b[a-f0-9]{32}\b",
    "hash_sha256": r"\b[a-f0-9]{64}\b",
}

def extract_iocs(text: str) -> dict:
    return {k: re.findall(v, text) for k, v in IOC_PATTERNS.items()}

1. Define Detection Scope — Identify the specific anomalies in industrial control systems techniques or indicators to hunt. Map to MITRE ATT&CK tactics/techniques where applicable.
2. Collect Baseline Data — Gather historical logs and establish normal behavior patterns for anomalies in industrial control systems.
3. Build Detection Queries — Write detection rules, Sigma rules, or SIEM queries targeting anomalies in industrial control systems indicators.
4. Execute Hunts — Run queries against the collected data, starting with broad filters and narrowing down.
5. Triage Results — Investigate alerts, filter false positives, and validate findings against known-good behavior.
6. Document Findings — Record confirmed detections, IOCs, and affected systems. Update detection rules based on findings.

Tools

• SIEM Platform — Central log aggregation and query execution
• Sigma Rules — Vendor-agnostic detection rule format
• MITRE ATT&CK Navigator — Technique mapping and coverage analysis

Process

1. Reconnaissance — Gather target information, identify attack surface, enumerate services
2. Analysis/Exploitation — Execute the technique, analyze results, document findings
3. Reporting — Document IOCs, write findings, provide remediation recommendations

Verification

• All anomalies in industrial control systems procedures executed completely and documented
• Findings validated against multiple data sources
• False positives identified and filtered
• Results documented with evidence and timestamps
• Recommendations provided with risk-based prioritization

Anti-Rationalization Table