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 …

Outliers are also referred to as abnormalities, discordants, deviants, or anomalies in the data
mining and statistics literature. In most applications, the data is created by one or more …

In this paper, we introduce autoencoder ensembles for unsupervised outlier detection. One
problem with neural networks is that they are sensitive to noise and often require large data …

The rapid evolution of technology has led to the generation of high dimensional data
streams in a wide range of fields, such as genomics, signal processing, and finance. The …

Ensemble analysis is a popular method used to improve the accuracy of various data mining
algorithms. Ensemble methods combine the outputs of multiple algorithms or base detectors …

This work addresses the outlier detection problem for feature-evolving streams, which has
not been studied before. In this setting both (1) data points may evolve, with feature values …

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 …

Given a stream of entries in a multi-aspect data setting ie, entries having multiple
dimensions, how can we detect anomalous activities in an unsupervised manner? For …

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 …

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 …