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"...

Full description

Saved in:
Bibliographic Details
Published in:IEEE transactions on circuits and systems. I, Regular papers Vol. 72; no. 6; pp. 2890 - 2902
Main Authors: Wang, Jun-Lan, Li, Xiao-Jian
Format: Journal Article
Language:English
Published: New York IEEE 01.06.2025
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
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
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1549-8328
1558-0806
DOI:10.1109/TCSI.2025.3559987