Power Passports for Fault Tolerance: Anomaly Detection in Industrial CPS Using Electrical EFB

We present how electrical Extra-Functional Behavioural (EFB) metrics can be used in generating accurate representations of executional units for industrial Cyber-Physical Systems (CPS). We achieve this by employing our concept of power passports (the representation), created per metric and per execution phase (executional unit). We employ these representations alongside supervised classification algorithms, i.e., Decision Tree and Random Forest, in an effective data analytical pipeline. Our approach is capable of detecting anomalous operational conditions and predicting the type of anomaly, out of different known types, with significant overall accuracies, as high as 99% in certain set-ups. We consider anomalous operational conditions as nonreference conditions, resulting in loss of performance or unreliable operation of a system. Our experiments are designed to reflect real-world conditions as much as possible and all of our collected raw data comes from real executions, normal and anomalous, with no synthetic manipulation. Our results show that a black box approach towards systems under scrutiny for anomaly detection and classification, given its accuracy and considering the limitations applicable to low-power industrial CPS, can be the preferred one.