Optimal Scheduling of Importance-Based DoS Attack in Multisystems

This article is concerned with the security of remote state estimation in multisystems. Different from most works, we study a novel denial-of-service (DoS) attack, which we call an importance-based DoS attack. Here, subsystem importance is defined as the quantitative impact of each subsystem on the overall state estimation accuracy, which is critical for ensuring correct decision-making. This work assumes that there are <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">N</i> subsystems, and the DoS attacker can only target <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">M</i> out of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">N</i> communication channels due to energy constraints. The attacker’s goal is to determine the optimal strategy to degrade the performance of remote state estimation. Two attack scenarios are considered: a model-based scenario where the attacker has knowledge of the overall dynamics and parameters of the system and a model-free scenario where the attacker lacks access to the wireless channel model. The problem is formulated as a Markov decision process (MDP) with an infinite time horizon. In addition, the optimal attack strategies corresponding to different importance function forms under various scenarios are considered, and solutions for some specific cases are provided. Numerical examples are presented to demonstrate the effectiveness of the proposed strategies.

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Publication Details

Journal
IEEE Sensors Journal
Published
2025-03-27
DOI
https://doi.org/10.1109/jsen.2025.3553093
Citations
1
Primary Topic
Cloud Computing and Resource Management
Type
article
Field-Weighted Citation Impact
3.25

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article

Optimal Scheduling of Importance-Based DoS Attack in Multisystems

Chun Liu, Xiaoqiang Ren, J. Wang, Xiaofan Wang et al.
1 citations
IEEE Sensors Journal
Cloud Computing and Resource Management
3.25
article

Optimal Scheduling of Importance-Based DoS Attack in Multisystems

Chun Liu, Xiaoqiang Ren, J. Wang, Xiaofan Wang, Hanxiao Liu
article en
1 citations

Abstract

This article is concerned with the security of remote state estimation in multisystems. Different from most works, we study a novel denial-of-service (DoS) attack, which we call an importance-based DoS attack. Here, subsystem importance is defined as the quantitative impact of each subsystem on the overall state estimation accuracy, which is critical for ensuring correct decision-making. This work assumes that there are <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">N</i> subsystems, and the DoS attacker can only target <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">M</i> out of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">N</i> communication channels due to energy constraints. The attacker’s goal is to determine the optimal strategy to degrade the performance of remote state estimation. Two attack scenarios are considered: a model-based scenario where the attacker has knowledge of the overall dynamics and parameters of the system and a model-free scenario where the attacker lacks access to the wireless channel model. The problem is formulated as a Markov decision process (MDP) with an infinite time horizon. In addition, the optimal attack strategies corresponding to different importance function forms under various scenarios are considered, and solutions for some specific cases are provided. Numerical examples are presented to demonstrate the effectiveness of the proposed strategies.

IEEE Sensors JournalVol. 25(9)
Shanghai University (CN)
National Natural Science Foundation of China
Openalex Percentile: Top 10%
Cloud Computing and Resource Management
3.25
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Optimal Scheduling of Importance-Based DoS Attack in Multisystems — Chun Liu, Xiaoqiang Ren, et al. · IEEE Sensors Journal (2025) | TGRS Research Map | TGRS