Dissipative Estimating for Nonlinear Markov Systems With Protocol-Based Deception Attacks and Measurement Quantization

IF 10.5 1区 计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS IEEE Transactions on Cybernetics Pub Date : 2025-02-19 DOI:10.1109/TCYB.2025.3532547
Yuyan Wu;Huaicheng Yan;Meng Wang;Zhichen Li;Jun Cheng
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Abstract

This article investigates the asynchronous estimator design for the interval type-2interval type-2 (IT2) fuzzy Markov jump systems subject to dynamic quantization and deception attacks. From the perspective of the attacker, a novel protocol-based deception attackdeception attack (DA) strategy is proposed, which utilizes the information of quantized output to assess the importance degree of transmission signals. Furthermore, in order to conserve the limited energy of the adversary, the independent attack strategies are designed for different sensors. Besides, the hidden Markov modelhidden Markov model (HMM) is applied to observe the system mode. Employing the Lyapunov stability theory and linear matrix inequality method, the sufficient conditions are acquired to guarantee the strictly-dissipative performance of the estimation error. Finally, two examples are illustrated to confirm the efficacy of the designed estimator and the advantage of the proposed attack tactics.
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基于协议欺骗攻击的非线性马尔可夫系统耗散估计及测量量化
研究了区间2型区间2型(IT2)模糊马尔可夫跳变系统在动态量化和欺骗攻击下的异步估计器设计。从攻击者的角度出发,提出了一种新的基于协议的欺骗攻击——欺骗攻击(DA)策略,利用量化输出的信息来评估传输信号的重要程度。此外,为了节约敌方有限的能量,针对不同的传感器设计了独立的攻击策略。此外,采用隐马尔可夫模型(HMM)来观察系统模式。利用李雅普诺夫稳定性理论和线性矩阵不等式方法,得到了保证估计误差具有严格耗散性能的充分条件。最后,通过两个算例验证了所设计估计器的有效性和所提出攻击策略的优越性。
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来源期刊
IEEE Transactions on Cybernetics
IEEE Transactions on Cybernetics COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-COMPUTER SCIENCE, CYBERNETICS
CiteScore
25.40
自引率
11.00%
发文量
1869
期刊介绍: The scope of the IEEE Transactions on Cybernetics includes computational approaches to the field of cybernetics. Specifically, the transactions welcomes papers on communication and control across machines or machine, human, and organizations. The scope includes such areas as computational intelligence, computer vision, neural networks, genetic algorithms, machine learning, fuzzy systems, cognitive systems, decision making, and robotics, to the extent that they contribute to the theme of cybernetics or demonstrate an application of cybernetics principles.
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