On the Role of Re-Descending M-Estimators in Resilient Anomaly Detection for Smart Living CPS

Anomaly-based attack detection methods that rely on learning the benign profile of operation are commonly used for identifying data falsification attacks and faults in cyber-physical systems. However, most works do not assume the presence of attacks while training the anomaly detectors- and their impact on eventual anomaly detection performance during the test set. Some robust learning methods overcompensate mitigation which leads to increased false positives in the absence of attacks/threats during training. To achieve this balance, this paper proposes a framework to enhance the robustness of previous anomaly detection frameworks in smart living applications, by introducing three profound design changes for threshold learning of time series anomaly detectors:(1) Tukey biweight loss function instead of square loss function (2) adding quantile weights to regression errors of Tukey (3) modifying the definition of empirical cost function from MSE to the harmonic mean of quantile weighted Tukey losses. We show that these changes mitigate performance degradation in anomaly detectors caused by untargeted poisoning attacks during training- while is simultaneously able to prevent false alarms in the absence of such training set attacks. We evaluate our work using a proof of concept that uses state-of-the-art anomaly detection in smart living CPS that detects false data injection in smart metering.