Toward Adaptive and Real-Time IIoT Intrusion Detection: A Survey of GAN-Based Augmentation, Drift-Aware Learning, and Edge Intelligence

Abstract

The rapid evolution of sophisticated cyber threats has drastically increased the cybersecurity risks in Industrial Internet of Things environments due to the massive interconnection of industrial devices, sensors, programmable logic controllers, gateways, and edge computing infrastructures. Traditional intrusion detection systems are insufficient for modern IIoT networks due to challenges with dynamic attack behaviors, class imbalance, concept drift, and computational limitations of resource-constrained edge devices. Although machine learning and deep learning have significantly improved intrusion detection performance, current studies usually deal with these challenges separately and do not provide a holistic view of adaptive and real-time industrial internet of things (IIoT) cybersecurity. In this survey, we provide a structured narrative review of adaptive intrusion detection techniques, focusing on three emerging research directions, including GAN-based data augmentation, drift-aware learning, and Edge Intelligence. It provides a structured narrative review of machine learning, deep learning, and hybrid IDS models, benchmark IIoT datasets, and representative techniques addressing data imbalance, concept drift, and low-latency edge deployment. The survey further provides a comparative study of existing approaches in terms of detection capability, adaptability, computational efficiency, scalability, and deployment suitability. To fill the gap between those complementary research directions, the survey combines the literature into a unified reference architecture that merges GAN-based data augmentation, drift-aware learning, and Edge Intelligence to enable adaptive, real-time IIoT intrusion detection. Finally, we discuss key research challenges and future opportunities in autonomous, collaborative, and trustworthy IIoT cybersecurity, thus providing a practical roadmap for the development of next-generation intelligent intrusion detection systems.