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    王彩云, 李芳菲. 基于KL散度检测器下的最优线性欺诈攻击[J]. 华东理工大学学报(自然科学版), 2021, 47(5): 627-634. DOI: 10.14135/j.cnki.1006-3080.20200802001
    引用本文: 王彩云, 李芳菲. 基于KL散度检测器下的最优线性欺诈攻击[J]. 华东理工大学学报(自然科学版), 2021, 47(5): 627-634. DOI: 10.14135/j.cnki.1006-3080.20200802001
    WANG Caiyun, LI Fangfei. Optimal Linear Deception Attack Based on KL Divergence Detector[J]. Journal of East China University of Science and Technology, 2021, 47(5): 627-634. DOI: 10.14135/j.cnki.1006-3080.20200802001
    Citation: WANG Caiyun, LI Fangfei. Optimal Linear Deception Attack Based on KL Divergence Detector[J]. Journal of East China University of Science and Technology, 2021, 47(5): 627-634. DOI: 10.14135/j.cnki.1006-3080.20200802001

    基于KL散度检测器下的最优线性欺诈攻击

    Optimal Linear Deception Attack Based on KL Divergence Detector

    • 摘要: 信息物理系统 (Cyber-Physical System, CPS) 进行远程状态估计时,攻击者容易通过篡改无线传输的测量数据对系统进行攻击,从而对系统性能造成损失。根据攻击者对系统知识的了解程度,分两种情况研究了传输过程中容易发生的线性欺诈攻击,同时分析了在KL散度检测器下两种情况的估计性能以及最优攻击策略,并且将最优攻击问题转化为凸优化问题。最后,给出了一维情况下的最优攻击的闭式表达式以及使用数值仿真来验证所得结论的有效性。

       

      Abstract: When the cyber-physical system (Cyber-Physical System, CPS) performs remote state estimation, it is easy for an attacker to attack the system by tampering with wirelessly transmitted measurement data, etc., thereby causing loss of system performance. In order to defend against attacks, we need to fully understand the attacker's attack strategy. According to the attacker's understanding of the system knowledge, we divide the research into two situations: one is that the attacker has limited ability and cannot directly obtain the transmission data, but can use additional sensors to get a measurement; the other one is that the attacker has a good understanding of the system, and can either directly obtain the transmission data or use its own additional sensors to measure the data. We analyze estimation performance of the attacker's optimal linear deception attack strategy under the KL divergence detector for these two situations, and transform the optimal attack problem into a convex optimization problem. Finally, we give a closed-form expression of the optimal linear deception attack in a one dimensional situation. We compare the estimation error covariance caused by the optimal attack in the two cases, and conclude that the more the attacker understands the system knowledge and the greater the impact of the attack on system performance. At the same time, it is also compared with the existing literature in terms of estimation performance and optimal attack, and uses numerical simulation to verify the effectiveness of the proposed results.

       

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