Enhancing Anomaly Detection in IT Systems Using Machine Learning Techniques

Authors

Thembiso Tshisikhawe, Tshwane University of Technology, South Africa

Khuliso Sigama, Tshwane University of Technology, South Africa

Tlou Ramabu, Tshwane University of Technology, 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.YSXE3525

Publisher

IST-Africa Institute and IIMC International Information Management Corporation Ltd

Published in Ireland

Abstract

The use of Information Technology Systems (ITS) has grown rapidly worldwide. ITS has been significantly affected by performance issues, leading to system instability and degrading User Experience (UX). UX plays a significant role in customer satisfaction. Distributed system logs capture operational runtime information to simplify system troubleshooting. However, at a large scale and with the volume of performance metrics collected, traditional methods for detecting performance anomalies are infeasible. In this paper, we adopt the Design Science Research Methodology (DSRM) and Machine Learning (ML) to evaluate an anomaly-detection approach using Deep Log (DL), K-Means Classifier (KMC), Isolation Forest (IF), and Random Forest (RF). We evaluated the efficiency and effectiveness of ML algorithms to recommend suitable techniques for anomaly detection in ITS and to reduce the time engineers spend resolving production issues. In this study, we use simulated data metrics from Kaggle that include system errors and resource consumption encountered by the system during runtime. We have learned that IF thrives on handling high-dimensional data (ideal for anomaly detection) because it is based on isolation forests rather than on class balance.

Keywords

Information Technology System, Anomaly Detection, Machine Learning, Isolation Forest, Random Forest, K-Means Classifier, Deep Log, Decision Trees

Cite this paper

T. Tshisikhawe, K. Sigama and T. Ramabu (2026) "Enhancing Anomaly Detection in IT Systems Using Machine Learning Techniques", 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.YSXE3525

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).