Adeel Abbas
A New Ensemble-Based Intrusion Detection System for Internet of Things
Abbas, Adeel; Khan, Muazzam A.; Latif, Shahid; Ajaz, Maria; Shah, Awais Aziz; Ahmad, Jawad
Authors
Muazzam A. Khan
Shahid Latif
Maria Ajaz
Awais Aziz Shah
Dr Jawad Ahmad J.Ahmad@napier.ac.uk
Lecturer
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.
Citation
Abbas, A., Khan, M. A., Latif, S., Ajaz, M., Shah, A. A., & Ahmad, J. (2022). A New Ensemble-Based Intrusion Detection System for Internet of Things. Arabian Journal for Science and Engineering, 47, 1805-1819. https://doi.org/10.1007/s13369-021-06086-5
Journal Article Type | Article |
---|---|
Acceptance Date | Aug 12, 2021 |
Online Publication Date | Aug 30, 2021 |
Publication Date | 2022-02 |
Deposit Date | Sep 9, 2021 |
Publicly Available Date | Sep 9, 2021 |
Journal | Arabian Journal for Science and Engineering |
Electronic ISSN | 1319-8025 |
Publisher | Springer |
Peer Reviewed | Peer Reviewed |
Volume | 47 |
Pages | 1805-1819 |
DOI | https://doi.org/10.1007/s13369-021-06086-5 |
Keywords | Intrusion detection, IoT, Machine learning, Security, Anomaly detection, Ensemble learning |
Public URL | http://researchrepository.napier.ac.uk/Output/2800771 |
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This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/
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