Title: Sliding window assisted mutual redundancy-based feature selection for intrusion detection system

Authors: Thotakura Veeranna; Kiran Kumar Reddy

Addresses: Jawaharlal Nehru Technological University, Kakinada-533003, Andhra Pradesh, India ' Department of Computer Science, Krishna University, Machilipatnam-521001, Andhra Pradesh, India

Abstract: In this paper, we propose a sliding windowing based mutual redundancy assisted feature selection (MRFS) algorithm that finds the duplicate features analytically and selects optimal feature for detection. This MRFS evaluates the mutual redundancy between as well as within network traffic connections and then selects an optimal feature subset to represent each connection attribute. After feature selection, they are fed to multi-class support vector machine (MC-SVM) based IDS for classification. The performance of MRFS-IDS is evaluated using a standard intrusion dataset, i.e., NSL-KDD and performance is measured in terms of accuracy and false alarm rate. The simulation results demonstrate that the proposed MRFS-IDS model has gained an accuracy of 95% approximately and false alarm rate of 0.70% which is much better than the counterpart methods.

Keywords: intrusion detection; data redundancy; mutual redundancy; sliding windowing; mutual information; multi-class support vector machine; MC-SVM; intrusion detection systems; IDSs; mutual redundancy-based feature selection; MRFS.

DOI: 10.1504/IJAHUC.2022.10048197

International Journal of Ad Hoc and Ubiquitous Computing, 2022 Vol.40 No.1/2/3, pp.176 - 186

Received: 21 Jan 2021
Accepted: 11 Mar 2021

Published online: 27 Jun 2022 *

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