Impact of Machine Learning on Intrusion Detection Systems for the Protection of Critical Infrastructure - read online

Aug 3, 2026

Robust intrusion detection systems (IDSs) are vital for protecting critical infrastructure and maintaining essential services. This study assesses the effectiveness of machine learning algorithms for anomaly detection on the Secure Water Treatment (SWaT) dataset, using time-series data from a water treatment testbed. Supervised learning models deliver high accuracy and reliable performance for diverse anomalies, leveraging spatial and temporal signals. Unsupervised methods provide labeled-data-free insights but incur higher false positive and false negative rates. Integrating both approaches can improve IDS resilience and supports practical algorithm selection for deployment.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages42

Reading Time1 h 46 min

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Abstract

1. Introduction

Critical infrastructure and cyber risk

Machine learning-based anomaly detection approach

Review Impact of Machine Learning on Intrusion Detection Systems for the Protection of Critical Infrastructure Avinash Kumar and Jairo A. Gutierrez * Academic Editors: Jiaping Gui and Futai Zou Received: 5 May 2025 Revised: 12 June 2025 Accepted: 17 June 2025 Published: 20 June 2025 Citation: Kumar, A.; Gutierrez, J.A. Impact of Machine Learning on Intrusion Detection Systems for the Protection of Critical Infrastructure. Information 2025, 16, 515. [https://](https://)[ ](https://)[doi.org/10.3390/info16070515](doi.org/10.3390/info16070515) [Copyright:](Copyright:) © 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://creativecommons.org/](https://creativecommons.org/)[ ](https://creativecommons.org/)[licenses/by/4.0/](licenses/by/4.0/)) . School of Engineering, Computer and Mathematical Sciences, Auckland University of Technology, 55 Wellesley Street West, Auckland 1010, New Zealand; [git.avinash24@gmail.com](git.avinash24@gmail.com) * [Correspondence: jairo.gutierrez@aut.ac.nz](Correspondence: jairo.gutierrez@aut.ac.nz) Abstract In the realm of critical infrastructure protection, robust intrusion detection systems (IDSs) are essential for securing essential services. This paper investigates the efficacy of various machine learning algorithms for anomaly detection within critical infrastructure, using the Secure Water Treatment (SWaT) dataset, a comprehensive collection of time-series data from a water treatment testbed, to experiment upon and analyze the findings. The study evaluates supervised learning algorithms alongside unsupervised learning algorithms. The analysis reveals that supervised learning algorithms exhibit exceptional performance with high accuracy and reliability, making them well-suited for handling the diverse and complex nature of anomalies in critical infrastructure. They demonstrate significant capabilities in capturing spatial and temporal variables. Among the unsupervised approaches, valuable insights into anomaly detection are provided without the necessity for labeled data, although they face challenges with higher rates of false positives and negatives. By outlining the benefits and drawbacks of these machine learning algorithms in relation to critical infrastructure, this research advances the field of cybersecurity. It emphasizes the importance of integrating supervised and unsupervised techniques to enhance the resilience of IDSs, ensuring the timely detection and mitigation of potential threats. The findings offer practical guidance for industry professionals on selecting and deploying effective machine learning algorithms in critical infrastructure environments. Keywords: intrusion detection systems; critical infrastructure 1. Introduction Modern human societies operate on an underlying structure of critical infrastructure, which is made up of a wide range of resources and systems that are necessary to maintain essential operations. These infrastructures are crucial to maintaining public safety, economic stability, and national security. They include the complex networks of electricity grids, transportation systems, water treatment plants, and financial institutions. But the same digital technologies that have ushered critical infrastructure sectors into the modern era have also made them a tempting target for malicious cyber actors looking for ways to undermine national security, disrupt operations, or steal sensitive data. Over the past decade, there has been a notable escalation in cyber threats targeting critical infrastructure sectors worldwide. As noted in [1], the 2015 cyberattack on Ukraine’s power grid, widely attributed to state-sponsored actors, stands as a stark example of the vulnerabilities inherent in such systems. This attack resulted in widespread blackouts that affected hundreds of thousands of p

FAQs

What dataset is used to evaluate machine learning for intrusion detection?

The study uses the Secure Water Treatment (SWaT) dataset, which contains time-series data from a water treatment testbed.

How do supervised and unsupervised learning algorithms compare in performance?

Supervised learning achieves exceptional performance with high accuracy and reliability, while unsupervised learning offers insights without labeled data but has higher false positive and false negative rates.

Why is integrating supervised and unsupervised techniques important for IDS resilience?

Combining them improves resilience by leveraging supervised models’ strength in handling complex anomalies and unsupervised methods’ ability to work without labeled data, supporting timely detection and mitigation.

Aug 3, 2026

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