Compressive sensing (CS) is a novel sampling paradigm that samples signals in a much
more efficient way than the established Nyquist sampling theorem. CS has recently gained a …

Anomalous behavior detection in many applications is becoming more and more important,
such as computer security, sensor network and so on. However, the inherent characteristics …

This paper addresses the anomaly detection problem in large-scale data mining
applications using residual subspace analysis. We are specifically concerned with situations …

In this work, we propose a novel approach called Operational Support Estimator Networks
(OSENs) for the support estimation task. Support Estimation (SE) is defined as finding the …

Determining anomalies in data streams that are collected and transformed from various
types of networks has recently attracted significant research interest. Principal Component …

Automated detection of abnormal events in video surveillance is an important task in
research and practical applications. This is, however, a challenging problem due to the …

Internal attacks are of a huge concern, because they are usually delicately masqueraded
under harmless-looking activities, which are very difficult to detect. Machine learning …

Anomaly detection in big data is a key problem in the big data analytics domain. In this
paper, the definitions of anomaly detection and big data were presented. Due to the …

Automated detection of abnormalities in data has been studied in research area in recent
years because of its diverse applications in practice including video surveillance, industrial …

Dynamically changing background (dynamic background) still presents a great challenge to
many motion-based video surveillance systems. In the context of event detection, it is a …