Monitors and detects behavioral anomalies in Industrial Control Systems (ICS) using machine learning and industrial protocol analysis.
This skill enables Claude to implement and manage anomaly detection systems specifically designed for Operational Technology (OT) environments. It leverages machine learning models like Isolation Forests and statistical baselining to identify deviations in deterministic SCADA communications, unauthorized protocol function codes, and rogue device connections. By correlating network traffic with physical process data from historians, it helps security engineers maintain the integrity and availability of critical infrastructure without relying solely on signature-based detection.
主要功能
01Statistical analysis of polling intervals and deterministic communication timing
02Multi-dimensional baselining of SCADA and OT network traffic patterns
034,121 GitHub stars
04Unsupervised machine learning for anomaly scoring using Isolation Forests
05Network topology mapping to identify rogue devices and unauthorized paths
06Detection of unauthorized Modbus, DNP3, and OPC UA function codes
使用场景
01Correlating network anomalies with physical process data for deep forensic analysis
02Deploying continuous security monitoring for OT environments lacking native IDS
03Investigating deviations in deterministic PLC-to-HMI communication patterns
