关键词: 信息物理融合系统（CPS）, 异常检测, 受限玻尔兹曼机（RBM）, 图模型

Abstract: Distributed Cyber-Physical Systems（CPS） often encounter physical faults and cyber anomalies by fault propagation due to strong connectivity among the sub-systems. This paper presents a new data-driven framework for system-wide anomaly detection for such issues. The framework based on spatiotemporal feature extraction built on the concept of symbolic dynamics for discovering and representing the interactions among the sub-systems of a CPS, and then learn a system-wide model via Restricted Boltzmann Machine（RBM） use the extracted features. The results show that the framework can capture multiple nominal modes with one graphical model, and can used for anomaly detection.

Key words: Cyber-Physical Systems（CPS）, anomaly detection, Restricted Boltzmann Machine（RBM）, graphical model