Machine Learning-Based Security Mechanisms for Threat Detection in Smart Manufacturing

Authors

Siyabonga Nkambule, University of Fort Hare, South Africa

Siyabulela Dyakalashe, University of Fort Hare, South Africa

Published in

IST-Africa 2026 Conference Proceedings

ISSN: 2576-8581

ISBN: 978-1-905824-76-2

DOI: https://doi.org/10.67725/IST-Africa.2026.SGZT6750

Publisher

IST-Africa Institute and IIMC International Information Management Corporation Ltd

Published in Ireland

Abstract

Smart Manufacturing, integrating emerging technologies, has transformed production efficiency but has simultaneously heightened exposure to cyber threats. The interconnective nature of these environments introduces vulnerabilities to ransomware, data breaches, and supply chain attacks, making robust cybersecurity mechanisms imperative. Reviewed literature suggests that traditional security measures often fail to address the dynamic nature of smart manufacturing threats effectively. This study investigates machine learning-based threat detection through Logistic Regression (LR) and Random Forest (RF) models, employing the NSL-KDD and CICIDS2017 datasets to enhance cybersecurity in smart manufacturing environments. Results indicate that RF achieved the highest accuracy (99.97% on CICIDS2017 and 99.77% on NSL-KDD), while LR performed competitively (99.52% and 99.11%, respectively), offering optimised efficiency in low-computation scenarios. Although both models demonstrated strong adaptability across datasets of varying sizes, the study is limited by reliance on benchmarked datasets rather than real-world industrial data. For future applications, we recommend a hybrid LR-RF detection framework in real-world smart factory settings, as it combines robustness with computational efficiency.

Keywords

Smart Manufacturing, Machine Learning, Intrusion and Threat Detection, Cybersecurity

Cite this paper

S. Nkambule and S. Dyakalashe (2026) "Machine Learning-Based Security Mechanisms for Threat Detection in Smart Manufacturing", IST-Africa 2026 Conference Proceedings, Miriam Cunningham and Paul Cunningham (Eds), IST-Africa Institute and IIMC, 2026, ISSN: 2576-8581, ISBN: 978-1-905824-76-2, https://doi.org/10.67725/IST-Africa.2026.SGZT6750

Access Paper

Click here to access this paper

IST-Africa Community Members can download papers by logging into their existing account.

If you wish to become a member of IST-Africa Community, please register your profile and you will then receive login details which you can use to access the papers.

Access to papers in the IST-Africa 2026 Conference Proceedings is for INDIVIDUAL USE only. Neither the Conference Proceedings (or individual double blind peer reviewed papers) are to be included in any public or institutional repository or processed in the context of any AI tool or large language model without prior written permission from the Publishers.

While the IST-Africa Institute and IIMC retain copyright of the Conference Proceedings, each author retains copyright to their individual paper. No paper or part thereof may be reproduced without the written permission of both the Conference Proceedings Publishers and appropriate author(s).