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Lookup NU author(s): Monika Roopak,
Professor Gui Yun Tian,
Professor Jonathon Chambers
This is the authors' accepted manuscript of an article that has been published in its final definitive form by Institution of Engineering and Technology, 2020.
For re-use rights please refer to the publisher's terms and conditions.
© The Institution of Engineering and Technology 2020.In this study, the authors propose a multi-objective optimisation-based feature selection (FS) method for the detection of distributed denial of service (DDoS) attacks in an internet of things (IoT) network. An intrusion detection system (IDS) is one approach for the detection of cyber-attacks. FS is required to reduce the dimensionality of data and improve the performance of the IDS. One of the reasons for the failure of an IDS is incorrect selection of features because most of the FS methods are based on a limited number of objectives such as accuracy or relevance of data, but these are not enough as they can be misleading for attack detection the contribution of this work is to develop appropriate FS method. They have implemented the nondominated sorting algorithm with its adapted jumping gene operator to solve the optimisation problem and exploited an extreme learning machine as the classifier for FS based on six important objectives for an IoT network. Experimental results verify that the proposed method performs well for FS and have achieved 99.9% and has reduced the total number of features by nearly 90%. The proposed method outperforms other proposed FS methods for the detection of DDoS attacks by an IDS.
Author(s): Roopak M, Tian GY, Chambers J
Publication type: Article
Publication status: Published
Journal: IET Networks
Online publication date: 13/05/2020
Acceptance date: 02/04/2016
Date deposited: 03/08/2020
ISSN (print): 2047-4954
ISSN (electronic): 2047-4962
Publisher: Institution of Engineering and Technology
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