In recent years, unsupervised feature selection methods have raised considerable interest in
many research areas; this is mainly due to their ability to identify and select relevant features …

In the present world huge amounts of data are stored and transferred from one location to
another. The data when transferred or stored is primed exposed to attack. Although various …

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 …

Network anomaly detection is an important and dynamic research area. Many network
intrusion detection methods and systems (NIDS) have been proposed in the literature. In this …

Relevant feature identification has become an essential task to apply data mining algorithms
effectively in real-world scenarios. Therefore, many feature selection methods have been …

With the growing popularity of shared resources, large volumes of complex data of different
types are collected automatically. Traditional data mining algorithms generally have …

With the advent of high dimensionality, adequate identification of relevant features of the
data has become indispensable in real-world scenarios. In this context, the importance of …

Anomaly detection is an important problem that has been researched within diverse
research areas and application domains. Many anomaly detection techniques have been …

Due to the frequency of malicious network activities and network policy violations, intrusion
detection systems (IDSs) have emerged as a group of methods that combats the …

This paper introduces concepts and algorithms of feature selection, surveys existing feature
selection algorithms for classification and clustering, groups and compares different …