论文标题
对远程状态估计的最佳线性攻击策略
An Optimal Linear Attack Strategy on Remote State Estimation
论文作者
论文摘要
这项工作考虑了在严格的隐形状态和$ε$ - 攻击的攻击状态下，设计远程估计攻击策略的问题。假定攻击者能够发射线性攻击以修改传感器数据。采用了基于Kullback-Leibler差异的度量标准来量化攻击的隐身性。我们根据过去的攻击信号和最新创新提出了广义的线性攻击。我们证明，与最近在文献中研究的线性攻击策略相比，提出的方法可以获得可能导致估计性损失更多的攻击。因此，结果为可用信息和攻击性能之间的权衡提供了限制，这在缓解策略的制定中很有用。最后，给出了一些数值示例，以评估提出的策略的性能。
This work considers the problem of designing an attack strategy on remote state estimation under the condition of strict stealthiness and $ε$-stealthiness of the attack. An attacker is assumed to be able to launch a linear attack to modify sensor data. A metric based on Kullback-Leibler divergence is adopted to quantify the stealthiness of the attack. We propose a generalized linear attack based on past attack signals and the latest innovation. We prove that the proposed approach can obtain an attack that can cause more estimation performance loss than linear attack strategies recently studied in the literature. The result thus provides a bound on the tradeoff between available information and attack performance, which is useful in the development of mitigation strategies. Finally, some numerical examples are given to evaluate the performance of the proposed strategy.
