Title: Development of adaptive AdaBoost classifier with optimal feature selection for enhanced intrusion detection in IoT

Authors: Mantripragada Yaswanth Bhanu Murthy; Thonduri Vineela; Godavarthi Amar Tej; Movva Vasantha Lakshmi

Addresses: Electronics and Communication Engineering, Vasireddy Venkatadri Institute of Technology, Guntur, Andhra Pradesh, India ' Electronics and Communication Engineering, Vasireddy Venkatadri Institute of Technology, Guntur, Andhra Pradesh, India ' Electronics and Communication Engineering, Vasireddy Venkatadri Institute of Technology, Guntur, Andhra Pradesh, India ' Electronics and Communication Engineering, Vasireddy Venkatadri Institute of Technology, Guntur, Andhra Pradesh, India

Abstract: The fundamental intention of this paper is to model IDS in IoT platforms using an adaptive AdaBoost classifier. In the data collection phase, the intrusion dataset of IoT is collected from the standard benchmark sources. Further, data cleaning is performed, which is to identify and remove errors and duplicate data. The subsequent phase after data cleaning is optimal feature selection, in which the relevant features by eradicating the redundant information are extracted to minimise the classifier complexity. The main contribution of the proposed model is the development of an adaptive AdaBoost classifier, where the tuning of the number of estimators is done by the novel hybrid active electrolocation-based group search optimisation (AE-GSO). From the results, the accuracy of the AE-GSO-AdaBoost is 0.93%, 1.89%, 0.83%, and 1.25% advanced than AdaBoost, RNN, DBN, and DNN, respectively. Based on the comparative study of state-of-the-art works, the numerical outcomes describe the promising part of the suggested techniques, offering comparable detection accuracy to the traditional methods between the accuracy and other performance metrics.

Keywords: enhanced intrusion detection; internet of things; IoT; adaptive AdaBoost classifier; optimal feature selection; active electrolocation-based group search optimisation; AE-GSO.

DOI: 10.1504/IJICS.2022.126757

International Journal of Information and Computer Security, 2022 Vol.19 No.1/2, pp.168 - 198

Received: 14 Apr 2021
Accepted: 04 Feb 2022
Published online: 04 Nov 2022 *

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