High-Confidence Attack Detection via Wasserstein-Metric Computations
High-Confidence Attack Detection via Wasserstein-Metric Computations
复制标题
通过 Wasserstein-Metric 计算进行高置信度攻击检测
作者：
Dan Li;S. Martínez
这封信考虑了线性网络物理系统的传感器攻击和故障检测问题，该系统受到可以服从未知光尾分布的系统噪声的影响。我们提出了一种新的基于阈值的检测机制，采用Wasserstein度量，并保证系统性能与有限数量的测量具有高置信度。建议的检测器可能会产生错误警报率<inline-formula><tex-math notation="LaTeX">$Delta $</tex-math></inline-formula>在正常操作中，其中<inline-formula><tex-math notation="LaTeX">$Delta $</tex-math></inline-formula>可以被调整到任意小<italic>的基准分布的</italic>装置。因此，所提出的检测器是敏感的传感器攻击和故障具有统计行为，这是不同的系统噪声。我们量化的影响<italic>隐形</italic>攻击开环稳定的系统扰动系统的运行，同时产生假警报符合自然系统噪声通过<italic>概率</italic>可达集。通过一个线性优化来计算检测措施和一个半定的程序来约束可达集，使易于实现。
This letter considers a sensor attack and fault detection problem for linear cyber-physical systems, which are subject to system noise that can obey an unknown light-tailed distribution. We propose a new threshold-based detection mechanism that employs the Wasserstein metric, and which guarantees system performance with high confidence with a finite number of measurements. The proposed detector may generate false alarms with a rate <inline-formula> <tex-math notation="LaTeX">$Delta $ </tex-math></inline-formula> in normal operation, where <inline-formula> <tex-math notation="LaTeX">$Delta $ </tex-math></inline-formula> can be tuned to be arbitrarily small by means of a <italic>benchmark distribution</italic>. Thus, the proposed detector is sensitive to sensor attacks and faults which have a statistical behavior that is different from that of the system noise. We quantify the impact of <italic>stealthy</italic> attacks on open-loop stable systems—which perturb the system operation while producing false alarms consistent with the natural system noise—via a <italic>probabilistic</italic> reachable set. Tractable implementation is enabled via a linear optimization to compute the detection measure and a semidefinite program to bound the reachable set.