Journal of Cybersecurity and Information Management JCIM 2690-6775 2769-7851 10.54216/JCIM https://www.americaspg.com/journals/show/4038 2019 2019 Middle Technical University, Iraq Omar Omar General Directorate of Education Anbar, 31001, Iraq Obaid Salim Department of Computer Engineering Techniques, College of Technical Engineering, University of Al Maarif, Al Anbar, 31001, Iraq Omar Muthanna Khudhur Department of Computer Networks Systems, College of Computer Science and Information Technology, University of Anbar, Ramadi, Iraq Shokhan M. Al Al-Barzinji College of Computer Science and Information Technology, University of Anbar, Anbar, Iraq Farah Maath Jasem 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. 2026 2026 97 112 10.54216/JCIM.170208 https://www.americaspg.com/articleinfo/2/show/4038