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In the context of cyber security, Intrusion Detection Systems (IDSs) help protect SCADA systems by monitoring for unauthorized access, malicious activity, and policy violations, providing a layer of defense against potential intrusions. Given the critical role of SCADA systems and the increasing cyber risks, this paper highlights the importance of transitioning from traditional signature-based IDS to advanced AI-driven methods. Particularly, this study tackles the issue of intrusion detection in SCADA systems, which are critical yet vulnerable parts of industrial control systems. Traditional Intrusion Detection Systems (IDSs) often fall short in SCADA environments due to data scarcity, class imbalance, and the need for specialized anomaly detection suited to industrial protocols like DNP3. By integrating GANs, this study mitigates these limitations by generating synthetic data, enhancing classification accuracy and robustness in detecting cyber threats targeting SCADA systems. Remarkably, the proposed GAN-based IDS achieves an outstanding accuracy of 99.136%, paired with impressive detection speed, meeting the crucial need for real-time threat identification in industrial contexts. Beyond these empirical advancements, this paper suggests future exploration of explainable AI techniques to improve the interpretability of IDS models tailored to SCADA environments. Additionally, it encourages collaboration between academia and industry to develop extensive datasets that accurately reflect SCADA network traffic.<\/jats:p>","DOI":"10.3390\/jcp5030073","type":"journal-article","created":{"date-parts":[[2025,9,12]],"date-time":"2025-09-12T12:08:16Z","timestamp":1757678896000},"page":"73","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Enhancing SCADA Security Using Generative Adversarial Network"],"prefix":"10.3390","volume":"5","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2072-3973","authenticated-orcid":false,"given":"Hong Nhung","family":"Nguyen","sequence":"first","affiliation":[{"name":"Department of AI and Software Engineering, School of Computing, Gachon University, Seongnam 13120, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2614-3424","authenticated-orcid":false,"given":"Jakeoung","family":"Koo","sequence":"additional","affiliation":[{"name":"Department of AI and Software Engineering, School of Computing, Gachon University, Seongnam 13120, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,9,12]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"46","DOI":"10.1016\/j.annemergmed.2023.04.025","article-title":"Hacking Acute Care: A Qualitative Study on the Health Care Impacts of Ransomware Attacks Against Hospitals","volume":"83","author":"Kusters","year":"2024","journal-title":"Ann. 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