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 …

The detection of anomalies in streaming data faces complexities that make traditional static
methods unsuitable due to computational costs and nonstationarity. We test and evaluate …

Anomaly detection from data streams is a hotly studied topic in the machine learning
domain. It is widely considered a challenging task because the underlying patterns exhibited …

Given a stream of entries over time in a multi-dimensional data setting where concept drift is
present, how can we detect anomalous activities? Most of the existing unsupervised …

Technology improvements have made it easier than ever to collect diverse telemetry at high
resolution from any cyber or physical system, for both monitoring and control. In the domain …

Real-time analytics and decision-making require online anomaly detection (OAD) to handle
drifts in data streams efficiently and effectively. Unfortunately, existing approaches are often …

Over the past couple of years, machine learning methods—especially the outlier detection
ones—have anchored in the cybersecurity field to detect network-based anomalies rooted in …

Machine learning ensembles combine multiple base models to produce a more accurate
output. They can be applied to a range of machine learning problems, including anomaly …

Anomalous behavior detection is an important component in many applications. Anomalies
can represent problematic situations where early detection is critical to make situational …

Intelligent transportation systems (ITS) enhance safety, comfort, transport efficiency, and
environmental conservation by allowing vehicles to communicate wirelessly with other …