Feature Selection Techniques in Intrusion Detection
Systems: A Review

 

 

 

Ahmad Salim1, Obaid Salim2, Omar Muthanna Khudhur3,*, Shokhan M. Al-Barzinji4, Farah Maath Jasem5

 

1Middle Technical University, Iraq

 

2General Directorate of Education Anbar, 31001, Iraq

 

3Department of Computer Engineering Techniques, College of Technical Engineering, University of Al Maarif, Al Anbar, 31001, Iraq

 

4Department of Computer Networks Systems, College of Computer Science and Information Technology, University of Anbar, Ramadi, Iraq

 

5College of Computer Science and Information Technology, University of Anbar, Anbar, Iraq

 

Emails: ahmadsalim@mtu.edu.iq; multiknowlge@gmail.com; omar.m.khudhur@uoa.edu.iq; shokhan.albarzinji@uoanbar.edu.iq; Farahmaath86@uoanbar.edu.iq    

 

 

 

 

 


Abstract

 

Intrusion detection has garnered significant attention as researchers strive to develop sophisticated models characterized by their high accuracy levels. However, the persistent challenge lies in creating reliable and effective intrusion detection systems capable of managing vast datasets under dynamic, real-time conditions. The effectiveness of such systems largely depends on the chosen detection methodologies, specifically the feature selection processes and the application of machine learning techniques. This paper offers a comprehensive review of feature selection methods employed in the realm of intrusion detection research. It examines various dimensionality reduction strategies, followed by a systematic classification of feature selection techniques to assess their impact on the training phase and subsequent detection efficacy. The focus was on the wrapper, filter feature selection methods, where the methods used were analysed, and their strengths and weaknesses were revealed. The identification and selection of the most pertinent features have been shown to significantly enhance the detection performance, not only in terms of accuracy but also in reducing computational demands, underscoring its critical importance in the architecture of intrusion detection systems.

Keywords: Network security; Intrusion detection; Machine learning; Feature selection; Wrapper; Filter