A comparative study of novelty detection models for zero day intrusion detection in industrial Internet of Things.

Otokwala, Uneneibotejit; Arifeen, Murshedul; Petrovski, Andrei

doi:10.1007/978-3-031-55568-8_20

A comparative study of novelty detection models for zero day intrusion detection in industrial Internet of Things.

Otokwala, Uneneibotejit; Arifeen, Murshedul; Petrovski, Andrei

Authors

UNENEIBOTEJIT OTOKWALA u.otokwala@rgu.ac.uk
Completed Research Student

Mr DIPTO ARIFEEN d.arifeen@rgu.ac.uk
Research Student

Andrei Petrovski

Contributors

George Panoutsos
Editor

Lyudmila S. Mihaylova
Editor

Mahdi Mahfouf
Editor

Abstract

The detection of zero-day attacks in the IoT network is a challenging task due to unknown security vulnerabilities. Also, the unavailability of the data makes it difficult to train a machine learning (ML) model about new vulnerabilities. The existing supervised ML-based Intrusion Detection Systems (IDS) are trained to detect only known attacks. On the contrary, the unsupervised ML-based IDSs show a high false-positive rate. In this paper, we experimented on three novelty detection algorithms named One-Class SVM (OCSVM), Local Outlier Factor (LOF), and Isolation Forest (IF), which follow the one-vs-all strategy for zero-day-intrusion detection for IoT datasets. UNSW-NB15 and IoTID20 datasets are considered for the experiment. Experimental results show that OCSVM outperformed the other two models for zero-day intrusion or unseen anomaly detection in IoT domain.

Citation

OTOKWALA, U., ARIFEEN, M. and PETROVSKI, A. 2024. A comparative study of novelty detection models for zero day intrusion detection in industrial Internet of Things. In Panoutsos, G., Mihaylova, L.S. and Mahfouf, M. (eds.) Advances in computational intelligence systems: contributions presented at the 21st UK workshop on computational intelligence (UKCCI 2022), 7-9 September 2022, Sheffield, UK. Advances in intelligent systems and computing, 1454. Cham: Springer [online], pages 238-249. Available from: https://doi.org/10.1007/978-3-031-55568-8_20

Presentation Conference Type	Conference Paper (published)
Conference Name	21st UK workshop on computational intelligence (UKCCI 2022)
Start Date	Sep 7, 2022
End Date	Sep 9, 2022
Acceptance Date	Jul 15, 2022
Online Publication Date	May 19, 2024
Publication Date	Dec 31, 2024
Deposit Date	Sep 10, 2024
Publicly Available Date	May 20, 2025
Publisher	Springer
Peer Reviewed	Peer Reviewed
Pages	238-249
Series Title	Advances in Intelligent Systems and Computing
Series Number	1454
Series ISSN	2194-5357; 2194-5365
Book Title	Advances in Computational Intelligence Systems
ISBN	9783031555671
DOI	https://doi.org/10.1007/978-3-031-55568-8_20
Keywords	\|IoT; Intrusion detection; OCSVM; LOF; IF; Security
Public URL	https://rgu-repository.worktribe.com/output/2472355

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Copyright Statement
© The Author(s), under exclusive licence to Springer Nature Switzerland. This version of the contribution has been accepted for publication, after peer review (when applicable) but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: https://doi.org/10.1007/978-3-031-55568-8_20. Use of this Accepted Version is subject to the publisher's Accepted Manuscript terms of use: https://www.springernature.com/gp/open-research/policies/accepted-manuscript-terms .