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  State Estimation Under Joint False Data Injection Attacks: Dealing With Constraints and Insecurity

Xu, W., Wang, Z., Hu, L., Kurths, J. (2022): State Estimation Under Joint False Data Injection Attacks: Dealing With Constraints and Insecurity. - IEEE Transactions on Automatic Control, 67, 12, 6745-6753.
https://doi.org/10.1109/TAC.2021.3131145

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 Creators:
Xu, Wenying1, Author              
Wang, Zidong 2, Author
Hu, Liang2, Author
Kurths, Jürgen1, Author              
Affiliations:
1Potsdam Institute for Climate Impact Research, ou_persistent13              
2External Organizations, ou_persistent22              

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 Abstract: This article is concerned with the security issue in the state estimation problem for a networked control system (NCS). A new model of joint false data injection (FDI) attack is established wherein attacks are injected to both the remote estimator and the communication channels. Such a model is general that includes most existing FDI attack models as special cases. The joint FDI attacks are subjected to limited access and/or resource constraints, and this gives rise to a few attack scenarios to be examined one by one. Our objective is to establish the so-called insecurity conditions under which there exists an attack sequence capable of driving the estimation bias to infinity while bypassing the anomaly detector. By resorting to the generalized inverse theory, necessary and sufficient conditions are derived for the insecurity under different attack scenarios. Subsequently, easy-to-implement algorithms are proposed to generate attack sequences on insecure NCSs with respect to different attack scenarios. In particular, by using a matrix splitting technique, the constraint-induced sparsity of the attack vectors is dedicatedly investigated. Finally, several numerical examples are presented to verify the effectiveness of the proposed FDI attacks.

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Language(s): eng - English
 Dates: 2022-11-262022-12-01
 Publication Status: Finally published
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: Peer
 Identifiers: DOI: 10.1109/TAC.2021.3131145
MDB-ID: No data to archive
PIKDOMAIN: RD4 - Complexity Science
Organisational keyword: RD4 - Complexity Science
Working Group: Network- and machine-learning-based prediction of extreme events
Research topic keyword: Complex Networks
Research topic keyword: Nonlinear Dynamics
Model / method: Machine Learning
 Degree: -

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Title: IEEE Transactions on Automatic Control
Source Genre: Journal, SCI, Scopus
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Pages: - Volume / Issue: 67 (12) Sequence Number: - Start / End Page: 6745 - 6753 Identifier: CoNE: https://publications.pik-potsdam.de/cone/journals/resource/ieee-transactions-automatic-control
Publisher: Institute of Electrical and Electronics Engineers (IEEE)