TransEC-GAN: A Transformer-Enhanced IDS for Robust Detection and Privacy in Industrial CPS

Despite the widespread deployment of various Intrusion Detection Systems (IDSs) in industrial Cyber-Physical Systems (CPS), significant challenges such as class imbalance, zero-day attacks, and privacy vulnerabilities persist. These issues underline the critical need for a more robust IDS solution that not only improves detection and generalization capabilities across different scenarios but also ensures stringent data privacy. In this paper, a novel IDS solution tailored for industrial CPS is proposed, designated as a Transformer-enhanced External Classifier-Generative Adversarial Network (TransEC-GAN). This innovative model extends the External Classifier-Generative Adversarial Network (EC-GAN) by integrating Transformer encoders, which leverage Wasserstein distance and label conditioning to enable stable gradient descent within the semi-supervised learning environment. To further fortify the system, Adaptive Differential Privacy (ADP) is incorporated, dynamically adjusting privacy settings to effectively prevent adversaries from exploiting sensitive information. Additionally, the proposed TransEC-GAN features a meticulously designed two-stage detection architecture that proficiently distinguishes between In-Distribution (InD) and Out-Of-Distribution (OOD) samples, enhancing its ability to identify and react to novel and evolving threats. Comprehensive experimental evaluations and theoretical analysis validate that the proposed TransEC-GAN not only safeguards against privacy breaches but also excels in detecting a wide array of attack types in industrial CPS settings.