In the past two decades, most research on anomaly detection has focused on improving the
accuracy of the detection, while largely ignoring the explainability of the corresponding …

This article provides a thorough meta-analysis of the anomaly detection problem. To
accomplish this we first identify approaches to benchmarking anomaly detection algorithms …

In this paper, we consider the problem of anomaly detection. Previous studies mostly deal
with this task in either supervised or unsupervised manner according to whether label …

Anomaly detection has been used for decades to identify and extract anomalous
components from data. Many techniques have been used to detect anomalies. One of the …

Anomaly detection has become an important topic in many domains with many different
solutions proposed until now. Despite that, there are only a few anomaly detection methods …

Anomaly detection is an important problem that has been researched within diverse
research areas and application domains. Many anomaly detection techniques have been …

Recent studies give more attention to the anomaly detection (AD) methods that can leverage
a handful of labeled anomalies along with abundant unlabeled data. These existing …

Anomaly detection has been a long-standing security approach with versatile applications,
ranging from securing server programs in critical environments, to detecting insider threats …

In one-class-learning tasks, only the normal case (foreground) can be modeled with data,
whereas the variation of all possible anomalies is too erratic to be described by samples …

Anomaly detection methods identify examples that do not follow the expected behaviour,
typically in an unsupervised fashion, by assigning real-valued anomaly scores to the …