| Title | A Simple Approach to Data-driven Security Detection for Industrial Cyber-Physical Systems |
| Publication Type | Conference Paper |
| Year of Publication | 2022 |
| Authors | Liu, Bin, Chen, Jingzhao, Hu, Yong |
| Conference Name | 2022 34th Chinese Control and Decision Conference (CCDC) |
| Keywords | Collaboration, Complexity theory, composability, Cost function, cps security, Cyber-physical systems, Data models, data-driven, Detectors, Estimation, false data injection attack, Human Behavior, industrial cyber-physical systems, policy-based governance, Power systems, pubcrawl, resilience, Resiliency, Scalability, security detection |
| Abstract | In this paper, a data-driven security detection approach is proposed in a simple manner. The detector is designed to deal with false data injection attacks suffered by industrial cyber-physical systems with unknown model information. First, the attacks are modeled from the perspective of the generalized plant mismatch, rather than the operating data being tampered. Second, some subsystems are selected to reduce the design complexity of the detector, and based on them, an output estimator with iterative form is presented in a theoretical way. Then, a security detector is constructed based on the proposed estimator and its cost function. Finally, the effectiveness of the proposed approach is verified by simulations of a Western States Coordinated Council 9-bus power system. |
| DOI | 10.1109/CCDC55256.2022.10033885 |
| Citation Key | liu_simple_2022 |
A Simple Approach to Data-driven Security Detection for Industrial Cyber-Physical Systems