Multi‐objective‐based feature selection for DDoS attack detection in IoT networks. Issue 3 (1st May 2020)

• Record Type: Journal Article
• Authors: Roopak, Monika; Tian, Gui Yun; Chambers, Jonathon
• Is Part Of: IET networks. Volume 9:Issue 3(2020)
• Page Start: 120
• Page End: 127
• Publication Date: 2020-05-01
• Subjects: feature extraction -- learning (artificial intelligence) -- sorting -- computer network security -- Internet of Things -- computer network reliability -- failure analysis -- pattern classification -- feature selection -- data reduction; IoT network -- FS methods -- IDS -- DDoS attack detection -- multiobjective optimisation‐based feature selection method -- service attacks -- Internet of Things network -- intrusion detection system -- cyber‐attack detection -- distributed denial of service attack detection -- real‐world measurements -- data dimensionality reduction -- feature extraction -- nondominated sorting algorithm -- jumping gene operator -- optimisation problem -- extreme learning machine -- FS classifier; Computer network architectures -- Periodicals; Computer network protocols -- Periodicals; Information networks -- Periodicals; Telecommunication systems -- Periodicals; 004.605
• DOI: 10.1049/iet-net.2018.5206
• Languages: English
• ISSNs: 2047-4954

Abstract

Abstract : 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.