The detection of anomalies in time series has gained ample academic and industrial
attention. However, no comprehensive benchmark exists to evaluate time-series anomaly …

Anomaly Detection is an unsupervised learning task aimed at detecting anomalous
behaviors with respect to historical data. In particular, multivariate Anomaly Detection has an …

Abstract Data-driven Anomaly Detection approaches have received increasing attention in
many application areas in the past few years as a tool to monitor complex systems in …

With the increasing demand for real-time analytics and decision making, anomaly detection
methods need to operate over streams of values and handle drifts in data distribution …

Anomaly detection is a fundamental task for time-series analytics with important implications
for the downstream performance of many applications. Despite increasing academic interest …

In recent years, detecting anomalies in real-world computer networks has become a more
and more challenging task due to the steady increase of high-volume, high-speed and high …

Unsupervised anomaly detection tackles the problem of finding anomalies inside datasets
without the labels availability; since data tagging is typically hard or expensive to obtain …

Random forests are popular methods for classification and regression, and many different
variants have been proposed in recent years. One interesting example is the Mondrian …

Insider threats have increasingly become a critical issue that modern enterprises and
organizations faced. They are mainly initiated by insider attackers, which may cause …

Anomaly detection is an important field in data science that has been widely researched and
applied, generating many methods. Among these methods, the isolation forest algorithm is …