Detection of anomaly in streaming data requires continuous analysis of the stream in real
time. This process turns out to be difficult due to varied volume and velocity of data streams …

With the rapid development of emerging technologies such as self-media, the Internet of
Things, and cloud computing, massive data applications are crossing the threshold of the …

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 …

A massive amount of real-time and evolving streaming data are produced from various
devices and applications. Anomaly detection is one of the main tasks of streaming data …

Detecting anomalies in streaming data is an important issue in a variety of real-word
applications as it provides some critical information, eg, Cyber security attacks, Fraud …

In this paper, we present the first open-source implementation of the robust random cut
forest (RRCF) algorithm—an unsupervised ensemble method for anomaly detection on …

Many distributed systems continuously gather, produce and elaborate data, often as data
streams that can change over time. Discovering anomalous data is fundamental to obtain …

Anomaly detection in multivariate data is an import research field. Many studies have been
proposed aiming to develop the local outlier factor (LOF). However, the existing LOF-based …

Anomaly detection in data streams has become a major research problem in the era of
ubiquitous sensing. We are collecting large amounts of data from non-stationary …

With the Internet of Things (IoT) devices becoming an integral part of human life, the need for
robust anomaly detection in streaming data has also been elevated. Dozens of distance …