Title: Hybrid ensemble techniques used for classifier and feature selection in intrusion detection systems

Authors: Ankit Kharwar; Devendra Thakor

Addresses: Chhotubhai Gopalbhai Patel Institute of Technology, Uka Tarsadia University, Bardoli, Gujarat, India ' Chhotubhai Gopalbhai Patel Institute of Technology, Uka Tarsadia University, Bardoli, Gujarat, India

Abstract: The data security of networks is a universal problem for governments, companies, and persons. The frequency of internet attacks has grown substantially, as have attacker strategies. The solution to this problem is intrusion detection, a typical and successful methodology for planning intrusion detection systems (IDS) with machine learning. The proposed IDS method consists of three stages: pre-processing, feature selection, and classification. We remove duplicate data and normalised data in our method's first stage. Sequential forward floating selection (SFFS) with extra-tree use for feature selection removes unwanted features in our method's second stage. LogitBoost with extra-tree classification to use selected features in our method third stage. The proposed method is evaluated on standard datasets KDD CUP'99, NSL-KDD, UNSW-NB15, CICIDS2017, and CICIDS2018. The experimental results show that the proposed method outperforms the existing work in terms of accuracy, false alarm rate, and detection rate.

Keywords: intrusion detection; anomaly detection; machine learning; ensemble methods; extra-tree; feature selection; sequential forward floating selection; SFFS; boosting algorithm; LogitBoost algorithm; network security.

DOI: 10.1504/IJCNDS.2022.123854

International Journal of Communication Networks and Distributed Systems, 2022 Vol.28 No.4, pp.389 - 413

Received: 30 Apr 2021
Accepted: 08 Aug 2021
Published online: 04 Jul 2022 *

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