Enhancing SCADA Security Using Generative Adversarial Network

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Detalhes bibliográficos
Publicado no:	Journal of Cybersecurity and Privacy vol. 5, no. 3 (2025), p. 73-96
Autor principal:	Nguyen, Hong Nhung
Outros Autores:	Koo Jakeoung
Publicado em:	MDPI AG
Assuntos:	Machine learning Accuracy Data integrity Deep learning Distributed network protocols Infrastructure Artificial intelligence Network security Intrusion detection systems Water treatment Communication Cybersecurity Malware Ransomware
Acesso em linha:	Citation/Abstract Full Text + Graphics Full Text - PDF
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Descrição
Resumo:	Supervisory Control and Data Acquisition (SCADA) systems play a critical role in industrial processes by providing real-time monitoring and control of equipment across large-scale, distributed operations. 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.
ISSN:	2624-800X
DOI:	10.3390/jcp5030073
Fonte:	ABI/INFORM Global