Analysis, detection and control of secure and safe cyber-physical control systems in a unified framework
Linlin Li, Steven X. Ding, Maiying Zhong, Dong Zhao, Yang Shi

TL;DR
This paper proposes a unified control-theoretic framework for analyzing, detecting, and mitigating faults and attacks in cyber-physical control systems, leveraging system input-output subspace analysis and coprime factorizations.
Contribution
It introduces a novel unified framework that combines control and detection in cyber-physical systems using system coprime factorizations and subspace analysis.
Findings
The framework effectively detects attacks and faults in cyber-physical systems.
It enables simultaneous control and detection in a unified manner.
The approach enhances resilience against cyber-physical threats.
Abstract
This paper deals with analysis, simultaneous detection of faults and attacks, fault-tolerant control and attack-resilient of cyber-physical control systems. In our recent work, it has been observed that an attack detector driven by an input residual signal is capable of reliably detecting attacks. In particular, observing system dynamics from the perspective of the system input-output signal space reveals that attacks and system uncertainties act on different system subspaces. These results motivate our exploration of secure and safe cyber-physical control systems in the unified framework of control and detection. The unified framework is proposed to handle control and detection issues uniformly and in subspaces of system input-output data. Its mathematical and control-theoretic basis is system coprime factorizations with Bezout identity at its core. We firstly explore those methods and…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsSmart Grid Security and Resilience · Adversarial Robustness in Machine Learning · Model Reduction and Neural Networks
