Anomaly Detection in Cyber-physical Systems: A SWAT Data-Based Mathematical modeling approach
This work presents statistical anomaly detectionmethods for cyber-physical systems using the Secure Water Treatment dataset, a scaled down version of a real-world industrial water treatment plant. We consider a critical sensor signal, the water level indicator LIT301, and set up two corresponding models for detecting cyber-attacks - a sliding window Z-score outlier detection, and an autoregressive integrated moving average time-series forecasting model of order (3,0,5).