Higher-Order Moment-Based Anomaly Detection
Higher-Order Moment-Based Anomaly Detection
复制标题
基于高阶矩的异常检测
作者：
Venkatraman Renganathan;Navid Hashemi;Justin Ruths;T. Summers
异常情况的识别是运行复杂、大规模和地理分布的网络物理系统的关键组成部分。在设计异常检测器时，通常假设高斯噪声模型以保持易处理；然而，这种假设可能导致实际的虚警率显著高于预期。在这里，我们使用有限和固定的检测测量数据的高阶矩来设计分布稳健的检测门限，以保证实际的虚警率是期望的虚警率的上限。此外，我们限定了通过隐形攻击的行动可以到达的状态，并确定了这种无法检测到的攻击的影响和最坏情况下的虚警率之间的权衡。通过数值实验，我们说明了高阶矩的知识如何导致阈值收紧，从而限制攻击者的潜在影响。
The identification of anomalies is a critical component of operating complex, large-scale and geographically distributed cyber-physical systems. While designing anomaly detectors, it is common to assume Gaussian noise models to maintain tractability; however, this assumption can lead to the actual false alarm rate being significantly higher than expected. Here we design a distributionally robust threshold of detection using finite and fixed higher-order moments of the detection measure data such that it guarantees the actual false alarm rate to be upper bounded by the desired one. Further, we bound the states reachable through the action of a stealthy attack and identify the trade-off between this impact of attacks that cannot be detected and the worst-case false alarm rate. Through numerical experiments, we illustrate how knowledge of higher-order moments results in a tightened threshold, thereby restricting an attacker’s potential impact.
影响因子：
4.2
作者：
M. J. Khojasteh;Anatoly Khina;M. Franceschetti;T. Javidi
通讯作者：
M. J. Khojasteh;Anatoly Khina;M. Franceschetti;T. Javidi