A new ensemble-based intrusion detection system for Internet of Things

Abbas, A., Khan, M. A., Latif, S., Ajaz, M., Shah, A. A. and Ahmad, J. (2022) A new ensemble-based intrusion detection system for Internet of Things. Arabian Journal for Science and Engineering, 47(2), pp. 1805-1819. (doi: 10.1007/s13369-021-06086-5)

Abstract

The domain of Internet of Things (IoT) has witnessed immense adaptability over the last few years by drastically transforming human lives to automate their ordinary daily tasks. This is achieved by interconnecting heterogeneous physical devices with different functionalities. Consequently, the rate of cyber threats has also been raised with the expansion of IoT networks which puts data integrity and stability on stake. In order to secure data from misuse and unusual attempts, several intrusion detection systems (IDSs) have been proposed to detect the malicious activities on the basis of predefined attack patterns. The rapid increase in such kind of attacks requires improvements in the existing IDS. Machine learning has become the key solution to improve intrusion detection systems. In this study, an ensemble-based intrusion detection model has been proposed. In the proposed model, logistic regression, naive Bayes, and decision tree have been deployed with voting classifier after analyzing model’s performance with some prominent existing state-of-the-art techniques. Moreover, the effectiveness of the proposed model has been analyzed using CICIDS2017 dataset. The results illustrate significant improvement in terms of accuracy as compared to existing models in terms of both binary and multi-class classification scenarios.

• Item Type: Articles
• Additional Information: Open access funding provided by Politecnico di Bari within the CRUI-CARE Agreement.
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Shah, Dr Awais Aziz
• Authors: Abbas, A., Khan, M. A., Latif, S., Ajaz, M., Shah, A. A., and Ahmad, J.
• College/School: College of Science and Engineering > School of Computing Science
• Journal Name: Arabian Journal for Science and Engineering
• Publisher: Springer
• ISSN: 2193-567X
• ISSN (Online): 2191-4281
• Published Online: 30 August 2021
• Copyright Holders: Copyright © 2021 The Authors
• First Published: First published in Arabian Journal for Science and Engineering 47(2):1805-1819
• Publisher Policy: Reproduced under a Creative Commons License