Time Series Anomaly Detection for Cyber-Physical Systems via Neural System Identification and Bayesian Filtering. (arXiv:2106.07992v2 [cs.LG] UPDATED)

Recent advances in AIoT technologies have led to an increasing popularity of
utilizing machine learning algorithms to detect operational failures for
cyber-physical systems (CPS). In its basic form, an anomaly detection module
monitors the sensor measurements and actuator states from the physical plant,
and detects anomalies in these measurements to identify abnormal operation
status. Nevertheless, building effective anomaly detection models for CPS is
rather challenging as the model has to accurately detect anomalies in presence
of highly complicated system dynamics …

anomaly detection cyber detection physical system systems