Anomalous signal detection for cyber-physical systems using interpretable causal neural network

Anomalous signal detection aims to detect unknown abnormal signals of machines from normal signals. However, building effective and interpretable anomaly detection models for safety-critical cyber-physical systems (CPS) is rather difficult due to the unidentified system noise and extremely intricate system dynamics of CPS and the neural network black box. This work proposes a novel time series anomalous signal detection model based on neural system identification and causal inference to track the dynamics of CPS in a dynamical state-space and avoid absorbing spurious correlation caused by confounding bias generated by system noise, which improves the stability, security and interpretability in detection of anomalous signals from CPS. Experiments on three real-world CPS datasets show that the proposed method achieved considerable improvements compared favorably to the state-of-the-art methods on anomalous signal detection in CPS. Moreover, the ablation study empirically demonstrates the efficiency of each component in our method.