Generative Adversarial Network-Based Network Intrusion Detection System for Supervisory Control and Data Acquisition System

In the context of burgeoning cyber threats targeting Supervisory Control and Data Acquisition (SCADA) systems, this paper presents a pioneering Intrusion Detection System (IDS) grounded in Generative Adversarial Networks (GANs). The ubiquity of information technology in critical industrial processes necessitates heightened security measures, and the proposed model addresses this imperative through the fusion of GANs and IDS tailored for SCADA environments. The study delves into the evolutionary trajectory of Intrusion Detection Systems, tracing their progression from conventional signature-based methods to the adaptive capabilities afforded by artificial intelligence, particularly deep learning. Against this backdrop, the paper investigates the vulnerabilities of SCADA systems, elucidating the sophisticated attack vectors identified in recent studies. The distributed nature of SCADA systems, deployed across extensive industrial landscapes, accentuates the need for robust intrusion detection mechanisms. This research's core innovation lies in applying a GAN model to SCADA systems, leveraging the distributed network protocol version 3 (DNP3) for data access authorization. The model is trained on a dataset comprising 12 meticulously selected features from DNP3 packets, attuning it to the nuances of SCADA network traffic. Notably, the proposed GAN-based IDS demonstrates exceptional efficacy, achieving impressive accuracy. Beyond the empirical contributions, the paper envisions future directions for integrating explainable AI techniques and enhancing the interpretability of IDS models specific to SCADA environments. Moreover, collaboration between academia and industry is advocated to foster the development of comprehensive datasets that represent SCADA network traffic.