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

Chao Liu; Sambuddha Ghosal; Zhanhong Jiang; Soumik Sarkar

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arXiv · 1512.07876

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

Chao Liu·Sambuddha Ghosal·Zhanhong Jiang·Soumik Sarkar

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Abstract

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.

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Chao Liu, Sambuddha Ghosal, Zhanhong Jiang, Soumik Sarkar. 2016-05-20. An unsupervised spatiotemporal graphical modeling approach to anomaly detection in distributed CPS. https://arxiv.org/abs/1512.07876

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