论文标题

具有随机辍学的受控状态估计系统的隐形黑客攻击和保密

Stealthy hacking and secrecy of controlled state estimation systems with random dropouts

论文作者

Lu, Jingyi, Quevedo, Daniel, Gupta, Vijay, Dey, Subhrakanti

论文摘要

我们研究了对手可以通过黑客入侵而无法检测到的最大信息增益。考虑一个传感器观察到的动态过程,该过程会根据配备了确认的无线通道(ACK)的某些参考传输策略,将系统状态的局部估计值传输到远程估计器。对手偷听传输,并主动劫持传感器以重新编程其传输策略。我们将完美的保密性定义为使平均预期误差协方差以合法的估计器限制,并且在对手处无限。通过分析预期误差协方差的固定分布,我们表明只有在ACK通道没有数据包辍学的情况下,才能获得不稳定系统的完美保密。在其他情况下,我们证明独立于参考策略和检测方法,无法实现完美的保密。在这种情况下,我们制定了一个受约束的马尔可夫决策过程,以得出对手应在传感器上实施的最佳传输策略,并设计一个stackelberg游戏,以得出合法估计器的最佳参考策略。

We study the maximum information gain that an adversary may obtain through hacking without being detected. Consider a dynamical process observed by a sensor that transmits a local estimate of the system state to a remote estimator according to some reference transmission policy across a packet-dropping wireless channel equipped with acknowledgments (ACK). An adversary overhears the transmissions and proactively hijacks the sensor to reprogram its transmission policy. We define perfect secrecy as keeping the averaged expected error covariance bounded at the legitimate estimator and unbounded at the adversary. By analyzing the stationary distribution of the expected error covariance, we show that perfect secrecy can be attained for unstable systems only if the ACK channel has no packet dropouts. In other situations, we prove that independent of the reference policy and the detection methods, perfect secrecy is not attainable. For this scenario, we formulate a constrained Markov decision process to derive the optimal transmission policy that the adversary should implement at the sensor, and devise a Stackelberg game to derive the optimal reference policy for the legitimate estimator.

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