Optimal Offline Privacy Schedule for Remote State Estimation under an Eavesdropper

Wang, Le; Cao, Xianghui; Sun, Changyin

doi:10.1109/yac.2019.8787719

Cited by 2 publications

(1 citation statement)

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“…For the security issues of a system's remote state estimation, there are many forms of malicious network attacks, but they are divided into three main and common categories: denial of service (DoS) attacks, integrity (including replay and false data injection) attacks and eavesdropping attacks [8]. DoS attacks are designed to interfere with wireless communication channels.…”

Section: Introductionmentioning

confidence: 99%

Allocation of Eavesdropping Attacks for Multi-System Remote State Estimation

Chang,

Peng,

Zhang

2024

Sensors

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In recent years, the problem of cyber–physical systems’ remote state estimations under eavesdropping attacks have been a source of concern. Aiming at the existence of eavesdroppers in multi-system CPSs, the optimal attack energy allocation problem based on a SINR (signal-to-noise ratio) remote state estimation is studied. Assume that there are N sensors, and these sensors use a shared wireless communication channel to send their state measurements to the remote estimator. Due to the limited power, eavesdroppers can only attack M channels out of N channels at most. Our goal is to use the Markov decision processes (MDP) method to maximize the eavesdropper’s state estimation error, so as to determine the eavesdropper’s optimal attack allocation. We propose a backward induction algorithm which uses MDP to obtain the optimal attack energy allocation strategy. Compared with the traditional induction algorithm, this algorithm has lower computational cost. Finally, the numerical simulation results verify the correctness of the theoretical analysis.

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Section: Introductionmentioning

confidence: 99%