Journal article
Data-Driven Undetectable Attack Against State Estimation in Distributed Control Systems
IEEE transactions on systems, man, and cybernetics. Systems, Vol.54(5), pp.3134-3143
05/2024
DOI: 10.1109/TSMC.2024.3356028
Abstract
This article investigates a data-driven design strategy of undetectable attacks against distributed control systems. The objective of the attacker is to worsen the estimation performance and maintain undetectability through compromising partial communication links. First, the existence condition of undetectable attacks is proposed based on the null space of system matrices. Then, the subspace identification method is applied to generate the null space and construct the undetectable attack sequence utilizing the gathered system input-output data. Besides, the impact of the undetectable attack is evaluated by solving an optimization problem involving attack undetectability and energy constraints. To launch the undetectable attack at any time during the system operation, an auxiliary attack sequence is designed. Finally, the simulation results verify the effectiveness of data-driven undetectable attacks.
Details
- Title: Subtitle
- Data-Driven Undetectable Attack Against State Estimation in Distributed Control Systems
- Creators
- Kai-Yu Wang - Northeastern UniversityDan Ye - Northeastern UniversityTian-Yu Zhang - College of Information Science and Engineering, Northeastern University, Shenyang, China
- Resource Type
- Journal article
- Publication Details
- IEEE transactions on systems, man, and cybernetics. Systems, Vol.54(5), pp.3134-3143
- Publisher
- IEEE
- DOI
- 10.1109/TSMC.2024.3356028
- ISSN
- 2168-2216
- eISSN
- 2168-2232
- Grant note
- 2023020359-JH6/1005 / Liaoning Distinguished Young Funds N2204008 / Fundamental Research Funds for the Central Universities (10.13039/501100012226) 62173071; U22A2067 / National Natural Science Foundation of China (10.13039/501100001809) BX20220061; 2023M730520 / China Postdoctoral Science Foundation (10.13039/501100002858)
- Language
- English
- Date published
- 05/2024
- Academic Unit
- Computer Science
- Record Identifier
- 9984696706602771
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