Title: Hybrid machine learning mechanism for intrusion detection systems in network security

Authors: Jean Roselio St Pierre; Yogesh Beeharry

Addresses: Department of Electrical and Electronic Engineering, Faculty of Engineering, University of Mauritius, Mauritius ' Department of Electrical and Electronic Engineering, Faculty of Engineering, University of Mauritius, Mauritius

Abstract: The rapid evolution of machine learning (ML) has also caused it to pave its way in the development of intelligent intrusion detection systems (IDSs). As such, the work in this paper looks at attacks involved in a communication network and the generation of a dataset for further use in training the ML models. The major contribution of this work is the implementation of a hybrid ML-based IDS. More specifically, the system operates using a combination of isolation forest for anomaly-based IDS (AIDS), random forest for signature-based intrusion detection system (SIDS) and feature selection method. The results demonstrate that the proposed hybrid model provides accuracies of above 98% and which is higher compared to the individual ML models by using a subset of eight features from the generated dataset.

Keywords: intrusion detection system; IDS; security threats; machine learning; hybrid model; random forest; GNS-3.

DOI: 10.1504/IJSPR.2022.128753

International Journal of Student Project Reporting, 2022 Vol.1 No.2, pp.166 - 187

Received: 15 Feb 2022
Accepted: 14 Oct 2022
Published online: 02 Feb 2023 *

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