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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 semisupervised 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.

Embedded digital devices, such as Field-Programmable Gate Arrays (FPGAs) and Systems on Chip (SoCs), are increasingly used in dependable or safety-critical systems. These commodity devices are subject to notable hardware ageing, which makes failures likely when used for an extended time. It is of vital importance to understand ageing processes and to detect hardware degradations early. In this survey, we describe the fundamental ageing mechanisms and review the main techniques for detecting ageing in FPGAs, microcontrollers, SoCs, and power supplies. The main goal of this work is to facilitate future research efforts in this field by presenting all main approaches in an organized way.

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