Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Sowmya, T.; Mary Anita, E.A. Title A name given to the resource A novel stable feature selection algorithm for machine learning based intrusion detection system Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived Procedia Computer Science;Volume;252;pp.738-747 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1016/j.procs.2025.01.034" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/j.procs.2025.01.034</a> <br /><br /><a href="https://www.scopus.com/pages/publications/85219199616?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/85219199616?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Sowmya T., Department of Computer Science and Engineering, Manipal Institute of Technology Bengaluru, Manipal Academy of Higher Education, Karnataka, Manipal, 576104, India, Department of Computer Science and Engineering, School of Engineering and Technology, Christ(Deemed to Be University), Bangalore, India; Mary Anita E.A., Department of Computer Science and Engineering, CHRIST (Deemed to Be University), Karnataka, Bengaluru, 560074, India Description An account of the resource The advent of new technologies like artificial intelligence, and big data has influenced many cyber attackers to launch their attacks on the network. Hence researchers have already proposed Intrusion Detection Systems by incorporating machine learning as well. Building an effective IDS is still a challenging task because of low accuracy. Managing high dimensional data is another major problem that occurs in IDS. Hence in this paper, an efficient Machine Learning based Intrusion Detection System is developed by means of a novel stable feature selection strategy called IV-RFE. The proposed methodology aims to select only the relevant features that contribute to the attack, which is purely based on relative variance, and weight factor in combination with RFE. This methodology increases the performance in terms of accuracy and maintains a stable set of features. Previous studies only focussed on the feature selection strategy and their performance. The feature stability also has to be considered which is an equally important metric, especially in the field of Intrusion Detection Systems. Hence in the current study, an efficient ML based IDS is proposed which selects only the relevant and stable features. Experimental results also revealed that the proposed IV-RFE outperformed well for three attacks with respect to accuracy and stability metrics also. The results show that stability is also an important indicator in selecting the features in the field of Intrusion Detection Systems. 2025 The Authors. Published by Elsevier B.V. Subject The topic of the resource Feature Selection(FS); Intrusion Detection System(IDS); Machine Learning; Recursive Feature Elimination(RFE) Publisher An entity responsible for making the resource available Elsevier B.V. Relation A related resource ISSN: 18770509; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource All Open Access; Gold Open Access Format The file format, physical medium, or dimensions of the resource online