Anomaly detection for resilient control systems using fuzzy-neural data fusion engine

Resilient control systems in critical infrastructures require increased cyber-security and state-awareness. One of the necessary conditions for achieving the desired high level of resiliency is timely reporting and understanding of the status and behavioral trends of the control system. This paper describes the design and development of a fuzzy-neural data fusion system for increased state-awareness of resilient control systems. The proposed system consists of a dedicated data fusion engine for each component of the control system. Each data fusion engine implements three-layered alarm system consisting of: 1) conventional threshold-based alarms, 2) anomalous behavior detector using self-organizing maps, and 3) prediction error based alarms using neural network based signal forecasting. The proposed system was integrated with a model of the Idaho National Laboratory Hytest facility, which is a testing facility for hybrid energy systems. Experimental results demonstrate that the implemented data fusion system provides timely plant performance monitoring and cyber-state reporting.