This paper focuses on designing measurement-based optimal stealthy attacks for linear cyber-physical systems (CPSs), where attackers aim to deteriorate the performance of remote state estimation and keep $\epsilon $ -stealthy to Kullback-Leibler divergence detector. Instead of constructing a linear attack directly, we consider a more general attack model with an arbitrary distribution to reveal the correlations between attacks and the stealth level $\epsilon $ . By solving a convex optimization problem, the mean and variance of optimal attacks on common unprotected systems are obtained. Not limited to zero mean Gaussian attacks, the derived optimal attacks can also obey non-zero mean Gaussian distribution and degenerate to the former in some special cases. For protected systems equipped with encryption procedures, a general criterion for the selection of attacked channels is given. Finally, an unmanned aerial vehicle (UAV) example is provided to verify the effectiveness of the theoretical results.

中文翻译：

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远程状态估计的基于测量的最优隐身攻击

本文的重点是为线性网络物理系统 (CPS) 设计基于测量的最优隐身攻击，其中攻击者旨在降低远程状态估计的性能并保持 $\epsilon $ -隐身到 Kullback-Leibler 散度检测器。我们不是直接构建线性攻击，而是考虑一个更通用的攻击模型，其具有任意分布，以揭示攻击与隐身级别之间的相关性 $\epsilon $ . 通过求解一个凸优化问题，得到了对常见无保护系统的最优攻击的均值和方差。不限于零均值高斯攻击，派生的最优攻击也可以服从非零均值高斯分布并在某些特殊情况下退化为前者。对于配备加密程序的受保护系统，给出了选择受攻击通道的一般标准。最后，通过一个无人机（UAV）实例验证了理论结果的有效性。