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Title

Taxonomy of Feature selection in Intrusion Detection System

Author

Vahid Kaviani Jabali, mina rahbari, armin kashkouli

Citation

Vol. 17  No. 6  pp. 88-102

Abstract

Although, using Internet for daily life and business has raised significantly but this popularity has brought enormous amount of risk by network attacks. Intrusion detection techniques is one most interesting research area in network security. Using IDS systems in networks can help to identify abnormal activities or detect attacks patterns to secure internal assets. In this literature, intrusion detection methods have been used by various machine learning approaches. In this article reviews the importance of security countermeasures. It begins with a background review on computer security and the taxonomy of Intrusion Detection and current technique of feature selection and drawing the taxonomy of intrusion detection system. This paper covers details of IDS design and development issues. It is studied for dimensionality reduction to find which means achieved a better accuracy and reduce workload, followed by existing techniques to compare a classifier and classifiers’ designs.

Keywords

Taxonomy, Feature selection, Detection, System.

URL

http://paper.ijcsns.org/07_book/201706/20170611.pdf