Modern distributed cyber-physical systems (CPSs) encounter a large variety of physical faults and cyber anomalies and in many cases, they are vulnerable to catastrophic fault propagation scenarios due to strong connectivity among the sub-systems. This paper presents a new data-driven framework for system-wide anomaly detection for addressing such issues. The framework is based on a spatiotemporal feature extraction scheme built on the concept of symbolic dynamics for discovering and representing causal interactions among the subsystems of a CPS. The extracted spatiotemporal features are then used to learn system-wide patterns via a Restricted Boltzmann Machine (RBM). The results show that: (1) the RBM free energy in the off-nominal conditions is different from that in the nominal conditions and can be used for anomaly detection; (2) the framework can capture multiple nominal modes with one graphical model; (3) the case studies with simulated data and an integrated building system validate the proposed approach.
Liu et al. REF presents a new data-driven framework for a spatiotemporal feature extraction scheme built on the concept of symbolic dynamics for discovering and representing causal interactions.
An Unsupervised Spatiotemporal Graphical Modeling Approach to Anomaly Detection in Distributed CPS
2016 ACM/IEEE 7th International Conference on Cyber-Physical Systems (ICCPS)
2016 ACM/IEEE 7th International Conference on Cyber-Physical Systems (ICCPS)
Mathematics Computer Science