Optimal Stealthy Linear Attack on Remote State Estimation With Side Information

Dan Ye; Bing Yang; Tian-Yu Zhang; Tianyu Zhang

doi:10.1109/JSYST.2021.3063735

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Optimal Stealthy Linear Attack on Remote State Estimation With Side Information

Journal article

Peer reviewed

Optimal Stealthy Linear Attack on Remote State Estimation With Side Information

Dan Ye, Bing Yang, Tian-Yu Zhang and Tianyu Zhang

IEEE systems journal, Vol.16(1), pp.1499-1507

03/01/2022

DOI: 10.1109/JSYST.2021.3063735

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Abstract

In this article, the innovation-based linear attack against state estimation in cyber-physical systems is studied. The scenario is considered where a sensor transmits its measurements to a remote estimator via a wireless channel. We propose an attack strategy to worsen the estimation performance of system when a passive detector is adopted. Different from the existing results, the side information is adopted synergistically besides the intercepted data. Meanwhile, the attacker needs to remain stealthy to the false-data detector. We analyze the performance degradation of system under the proposed attack strategy by deriving the remote estimation error covariance recursion. With the stealthiness constraint, the optimal stealthy attack can be obtained by solving a convex optimization problem accordingly. Besides, we provide a specific algorithm to calculate the falsified measurements. Finally, the effectiveness of explored attack strategy is demonstrated via simulation examples.

Cyber-physical systems (CPSs) security

Degradation

Denial-of-service attack

Detectors

Estimation

historical data

linear attack

remote state estimation

side information

State estimation

Technological innovation

Wireless sensor networks

Details

Title: Subtitle: Optimal Stealthy Linear Attack on Remote State Estimation With Side Information
Creators: Dan Ye - Northeastern University
Bing Yang - Northeastern University
Tian-Yu Zhang - College of Information Science and Engineering, Northeastern University, Shenyang, China
Tianyu Zhang - Computer Science
Resource Type: Journal article
Publication Details: IEEE systems journal, Vol.16(1), pp.1499-1507
Publisher: IEEE
DOI: 10.1109/JSYST.2021.3063735
ISSN: 1932-8184
eISSN: 1937-9234
Grant note: XLYC1907035 / LiaoNing Revitalization Talents Program (10.13039/501100018617) N2004026; N2004027 / Fundamental Research Funds for the Central Universities (10.13039/501100012226) 61773097; U1813214 / National Natural Science Foundation of China (10.13039/501100001809)
Language: English
Date published: 03/01/2022
Academic Unit: Computer Science
Record Identifier: 9984696567002771

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