Comparative Evaluation of Semi-Supervised Anomaly Detection Algorithms on High-Integrity Digital Systems

Anomaly detection algorithms solve the problem of identifying unexpected values in data sets. Such algorithms have been classically used for cleaning unlabelled data sets from potentially unwanted values. However, the ability to detect outlying values in data sets can also be used to detect anomalies in systems. Semi-supervised anomaly detection algorithms learn from data for known correct behavior. Such algorithms have been used in various fields, e.g., system security, fault detection, medical applications.In this paper, we use the Area Under the Receiver Operating Characteristic (AUROC) score to evaluate algorithms for semi-supervised anomaly detection when applied to high-integrity distributed digital systems. We identify the relevant parameter for each algorithm and observe how the parameter influences the score and the runtime.