Data-Driven Attack Detection and Identification for Cyber-Physical Systems Under Sparse Sensor Attacks: Iterative Reweighted l2/l1 Recovery Approach

This paper investigates the data-based attack detection and identification for cyber-physical systems (CPSs) under sparse sensor attacks. In order to improve the identification performance, a novel scheme based on an iterative reweighted <inline-formula> <tex-math notation="LaTeX"...

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Vydané v:IEEE transactions on circuits and systems. I, Regular papers Ročník 72; číslo 6; s. 2890 - 2902
Hlavní autori: Wang, Jun-Lan, Li, Xiao-Jian
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: New York IEEE 01.06.2025
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
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ISSN:1549-8328, 1558-0806
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Shrnutí:This paper investigates the data-based attack detection and identification for cyber-physical systems (CPSs) under sparse sensor attacks. In order to improve the identification performance, a novel scheme based on an iterative reweighted <inline-formula> <tex-math notation="LaTeX">l_{2}/l_{1} </tex-math></inline-formula> minimization algorithm is presented. Firstly, a threshold that characterizes the maximum number of identifiable attacks is determined. By introducing the reweighting technique, smaller weights are assigned to the relatively easy-to-identify attacks, namely, blocks with larger <inline-formula> <tex-math notation="LaTeX">l_{2} </tex-math></inline-formula>-norms, thus forcing the minimization to focus on the ones with smaller <inline-formula> <tex-math notation="LaTeX">l_{2} </tex-math></inline-formula>-norms. Then, the number of identifiable attacks is enhanced and a higher identification accuracy is guaranteed compared with the existing results. Finally, three examples are given to verify the effectiveness and advantages of the proposed scheme in both noisy and noiseless cases.
Bibliografia:ObjectType-Article-1
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ISSN:1549-8328
1558-0806
DOI:10.1109/TCSI.2025.3559987