A Revealing Large-Scale Evaluation of Unsupervised Anomaly Detection Algorithms. (arXiv:2204.09825v1 [cs.LG])

Anomaly detection has many applications ranging from bank-fraud detection and
cyber-threat detection to equipment maintenance and health monitoring. However,
choosing a suitable algorithm for a given application remains a challenging
design decision, often informed by the literature on anomaly detection
algorithms. We extensively reviewed twelve of the most popular unsupervised
anomaly detection methods. We observed that, so far, they have been compared
using inconsistent protocols - the choice of the class of interest or the
positive class, the split of …

algorithms anomaly detection detection large lg scale