乘性噪声模型不确定性下的异常检测
Anomaly Detection Under Multiplicative Noise Model Uncertainty
摘要：
状态估计器是用于监测网络物理系统的异常检测器的重要组成部分。许多常用的状态估计器容易受到模型风险的影响，因为它们严重依赖于准确的状态空间模型的可用性。建模误差使得更难以区分与预期行为的偏差是由于异常还是仅仅是缺乏对系统动态的了解。在这项研究中，我们通过乘性噪声框架来解释模型的不确定性。具体来说，我们建议在这种设置中使用基于乘性噪声LQG的补偿器来对冲模型不确定性风险。然后可以将来自估计器的残差的大小与阈值进行比较以检测异常。最后，提出的检测器进行了验证，使用数值模拟。扩展最先进的异常检测在网络物理系统中处理模型的不确定性是目前工作的主要新贡献。
英文摘要：
State estimators are crucial components of anomaly detectors that are used to monitor cyber-physical systems. Many frequently-used state estimators are susceptible to model risk as they rely critically on the availability of an accurate state-space model. Modeling errors make it more difficult to distinguish whether deviations from expected behavior are due to anomalies or simply a lack of knowledge about the system dynamics. In this research, we account for model uncertainty through a multiplicative noise framework. Specifically, we propose to use the multiplicative noise LQG based compensator in this setting to hedge against the model uncertainty risk. The size of the residual from the estimator can then be compared against a threshold to detect anomalies. Finally, the proposed detector is validated using numerical simulations. Extension of state-of-the-art anomaly detection in cyber-physical systems to handle model uncertainty represents the main novel contribution of the present work.
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