Identification of interdependencies and prediction of fault propagation for cyber–physical systems
本文用相关度量和启发式因果分析识别网络物理系统组件间的相互依赖关系，并用神经网络预测即将发生的故障，帮助系统操作员及时预防，减少事故和攻击后果。
Interdependence is an intrinsic feature of cyber–physical systems. Cyber and physical components are tightly integrated with each other, and hence, a trivial impairment in a part of the system may affect several components, leading to a sequence of failures that collapses the entire system. In this paper, we seek to identify the interdependencies among the components of a cyber–physical system using correlation metrics as well as a heuristic causation analysis method. We also demonstrate applicability of neural networks for prediction of imminent failures given the current system state. The proposed prediction tool can help system operators to perform timely preventive actions and mitigate the consequences of accidental failures and malicious attacks. As a case study, we have analyzed two smart grid test cases based on IEEE power bus systems, namely, IEEE-14 and IEEE-57.