Over the past decade, we have witnessed an enormous amount of research effort dedicated
to the design of efficient outlier detection techniques while taking into consideration …

Given an unsupervised outlier detection task, how should one select i) a detection algorithm,
and ii) associated hyperparameter values (jointly called a model)? E ective outlier model …

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 …

We present an extension to the model-free anomaly detection algorithm, Isolation Forest.
This extension, named Extended Isolation Forest (EIF), resolves issues with assignment of …

Knowledge graphs (KGs) could effectively integrate a large number of real-world assertions,
and improve the performance of various applications, such as recommendation and search …

Every year, criminals launder billions of dollars acquired from serious felonies (eg, terrorism,
drug smuggling, or human trafficking), harming countless people and economies …

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

Abstract System monitoring and anomaly detection is a crucial task in daily operation. With
the rapid development of cyber-physical systems and IT systems, multiple sensors get …

Performing anomaly detection on attributed networks concerns with finding nodes whose
patterns or behaviors deviate significantly from the majority of reference nodes. Its success …

High false-positive rate is a long-standing challenge for anomaly detection algorithms,
especially in high-stake applications. To identify the true anomalies, in practice, analysts or …