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Modern distributed cyber-physical systems (CPSs) encounter a large variety ofphysical faults and cyber anomalies and in many cases, they are vulnerable tocatastrophic fault propagation scenarios due to strong connectivity among thesub-systems. This paper presents a new data-driven framework for system-wideanomaly detection for addressing such issues. The framework is based on aspatiotemporal feature extraction scheme built on the concept of symbolicdynamics for discovering and representing causal interactions among thesubsystems of a CPS. The extracted spatiotemporal features are then used tolearn system-wide patterns via a Restricted Boltzmann Machine (RBM). Theresults show that: (1) the RBM free energy in the off-nominal conditions isdifferent from that in the nominal conditions and can be used for anomalydetection; (2) the framework can capture multiple nominal modes with onegraphical model; (3) the case studies with simulated data and an integratedbuilding system validate the proposed approach.

An unsupervised spatiotemporal graphical modeling approach to anomaly detection in distributed CPS