Towards a novel approach to enhance cyber security assessment of industrial energy control and distribution systems through generative adversarial networks

Abstract

This work explores the use of generative adversarial networks (GANs) to tackle cyber-security challenges, including threat identification, anomaly detection, and mitigation strategies, particularly in complex systems and critical infrastructures like industrial control systems for energy production and distribution. GANs address a key obstacle in machine learning (ML)-based systems: the scarcity of quality data for training models capable of fully leveraging ML and deep learning in cyber-security applications, such as intrusion detection systems and malicious behaviour detectors. The study highlights GANs’ potential to enhance data augmentation by generating realistic synthetic network traffic flows. These flows simulate common cyber-attacks targeting operational technologies (OTs) and information technologies (ITs). A primary contribution of this research is the creation of a large, high-quality dataset of OT and IT network traffic, designed to improve the robustness of ML models used in cyber-defense systems. Additionally, the work includes statistical analyses to evaluate the reliability of GAN-based data augmentation, laying the foundation for further research. This approach promises significant advancements in developing resilient ML models capable of addressing evolving cyber-security threats.