Leveraging Deep Reinforcement Learning Technique for Intrusion Detection in SCADA Infrastructure

The prevalence of cyber-attacks perpetrated over the last two decades, including coordinated attempts to breach targeted organizations, has drastically and systematically exposed some of the more critical vulnerabilities existing in our cyber ecosystem. Particularly in Supervisory Control and Data Acquisition (SCADA) systems with targeted attacks aiming to bypass signature-based protocols, attempting to gain control over operational processes. In the past, researchers utilized deep learning and reinforcement learning algorithms to mitigate threats against industrial control systems (ICS). However, as technology evolves, these techniques become ineffective in monitoring and enhancing the cybersecurity defenses of those system against unwanted attacks. To address these concerns, we propose a deep reinforcement learning (DRL) framework for anomaly detection in the SCADA network. Our model utilizes a "Q-network", which allows it to achieve state-of-the-art performance in pattern recognition from complex tasks. We validated our solution on two publicly available datasets. The WUSTL-IIoT-2018 and the WUSTL-IIoT-2021, each comprised of twenty-five networking features representing benign and attack traffic. The results obtained shows that our model successfully achieved an accuracy of 99.36% in attack detection, highlighting DRL's potential to enhance the security of critical infrastructure and laying the foundation for future research in this domain.