论文标题
通过Wasserstein-Metric计算进行高信心攻击检测
High-Confidence Attack Detection via Wasserstein-Metric Computations
论文作者
论文摘要
本文考虑了线性网络物理系统的传感器攻击和故障检测问题，该系统受可能遵守未知轻尾分布的系统噪声。我们提出了一种采用Wasserstein度量的新的基于阈值的检测机制，并通过使用有限数量的测量值来确保具有高置信度的系统性能。提出的检测器可以在正常操作中以$δ$的速率产生错误警报，其中$δ$可以通过基准分布来调用任意小的，这是我们机制的一部分。因此，所提出的检测器对传感器攻击和故障敏感，其统计行为与系统噪声不同。我们量化了隐秘攻击的影响 - - 旨在通过概率可及的集合产生与自然系统噪声一致的错误警报，同时产生与自然系统噪声一致的错误警报。为了实现我们的方法的可拖动实现，我们提出了一个线性优化问题，该问题计算提出的检测度量和一个产生拟议的可达集合的半决赛程序。
This paper considers a sensor attack and fault detection problem for linear cyber-physical systems, which are subject to system noise that can obey an unknown light-tailed distribution. We propose a new threshold-based detection mechanism that employs the Wasserstein metric, and which guarantees system performance with high confidence employing a finite number of measurements. The proposed detector may generate false alarms with a rate $Δ$ in normal operation, where $Δ$ can be tuned to be arbitrarily small by means of a benchmark distribution which is part of our mechanism. Thus, the proposed detector is sensitive to sensor attacks and faults which have a statistical behavior that is different from that of the system's noise. We quantify the impact of stealthy attacks---which aim to perturb the system operation while producing false alarms that are consistent with the natural system's noise---via a probabilistic reachable set. To enable tractable implementation of our methods, we propose a linear optimization problem that computes the proposed detection measure and a semidefinite program that produces the proposed reachable set.
