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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Veröffentlicht in:IEEE transactions on circuits and systems. I, Regular papers Jg. 72; H. 6; S. 2890 - 2902
Hauptverfasser: Wang, Jun-Lan, Li, Xiao-Jian
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York IEEE 01.06.2025
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Accuracy
Algorithms
Attack detection and identification
Cyber-physical systems
cyber-physical systems (CPSs)
data-driven
Iterative algorithms
iterative reweighted l₂/l₁ (IR-l₂/l₁) minimization algorithm
Mathematical models
Minimization
Noise
Noise measurement
Norms
Optimization
Security
sparse attacks
Vectors
ISSN:1549-8328, 1558-0806
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Abstract 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.
AbstractList 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.
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 [Formula Omitted] 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 [Formula Omitted]-norms, thus forcing the minimization to focus on the ones with smaller [Formula Omitted]-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.
Author Wang, Jun-Lan
Li, Xiao-Jian
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SubjectTerms Accuracy
Algorithms
Attack detection and identification
Cyber-physical systems
cyber-physical systems (CPSs)
data-driven
Iterative algorithms
iterative reweighted l₂/l₁ (IR-l₂/l₁) minimization algorithm
Mathematical models
Minimization
Noise
Noise measurement
Norms
Optimization
Security
sparse attacks
Vectors
Title Data-Driven Attack Detection and Identification for Cyber-Physical Systems Under Sparse Sensor Attacks: Iterative Reweighted l2/l1 Recovery Approach
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