A novel stable feature selection algorithm for machine learning based intrusion detection system

Title

A novel stable feature selection algorithm for machine learning based intrusion detection system

Creator

Sowmya, T.; Mary Anita, E.A.

Description

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.

Source

Procedia Computer Science;Volume;252;pp.738-747

Date

01-01-2025

Publisher

Elsevier B.V.

Subject

Feature Selection(FS); Intrusion Detection System(IDS); Machine Learning; Recursive Feature Elimination(RFE)

Coverage

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

Rights

All Open Access; Gold Open Access

Relation

ISSN: 18770509;

Format

online

Language

English

Type

Conference paper

Identifier

https://doi.org/10.1016/j.procs.2025.01.034

https://www.scopus.com/pages/publications/85219199616?origin=resultslist