Comprehensive Analysis of SCADA System Data for Intrusion Detection Using Machine Learning
Department of Computer Science, School of Computer Science Stripes Hall 44, Western Illinois University.
Research Article
Global Journal of Engineering and Technology Advances, 2025, 22(02), 064-089.
Article DOI: 10.30574/gjeta.2025.22.2.0027
Publication history:
Received on 31 December 2024; revised on 09 February 2025; accepted on 12 February 2025
Abstract:
This report investigates the implementation of advanced machine learning models within Supervisory Control and Data Acquisition (SCADA) systems to enhance intrusion detection capabilities and system security. By utilizing models such as CatBoost and XGBRegressor, which excel in processing complex, non-linear data, the study demonstrates significant improvements in predicting and managing operational states in wind turbines. The incorporation of Explainable AI (XAI) techniques, particularly SHAP values, further provides transparency in model decisions, fostering trust among stakeholders. Recommendations are provided for effective model integration, deployment with XAI features, and necessary policy enhancements to ensure the secure, reliable, and ethical use of AI in critical infrastructure environments.
Keywords:
Machine Learning; SCADA system; Intrusion Detection; Explainable AI (XAI); Critical Infrastructure; Cybersecurity; Policy Enhancements
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Copyright © 2025 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0