While anomaly detection is relatively well-studied, it remains a topic of ongoing interest and
challenge, as our society becomes increasingly interconnected and digitalized. In this paper …

Given a long list of anomaly detection algorithms developed in the last few decades, how do
they perform with regard to (i) varying levels of supervision,(ii) different types of anomalies …

Outlier detection refers to the identification of rare items that are deviant from the general
data distribution. Existing approaches suffer from high computational complexity, low …

Detecting outliers is a significant problem that has been studied in various research and
application areas. Researchers continue to design robust schemes to provide solutions to …

PyOD is an open-source Python toolbox for performing scalable outlier detection on
multivariate data. Uniquely, it provides access to a wide range of outlier detection …

Outlier detection refers to the identification of data points that deviate from a general data
distribution. Existing unsupervised approaches often suffer from high computational cost …

Mobile payment systems are becoming more popular due to the increase in the number of
smartphones, which, in turn, attracts the interest of fraudsters. Extant research has therefore …

An outlier is an event or observation that is defined as an unusual activity, intrusion, or a
suspicious data point that lies at an irregular distance from a population. The definition of an …

With a long history of traditional Graph Anomaly Detection (GAD) algorithms and recently
popular Graph Neural Networks (GNNs), it is still not clear (1) how they perform under a …

In unsupervised outlier ensembles, the absence of ground truth makes the combination of
base outlier detectors a challenging task. Specifically, existing parallel outlier ensembles …