Artificial Intelligence-Driven Cybersecurity Framework for Industrial Control Networks

Abstract

Automating and controlling systems in energy, water supply, transportation and manufacturing now greatly relies on Industrial Control Networks (ICNs). As OT and IT systems merge with the help of ICNs, these networks are now both more integrated and smart. However, this introduces more cybersecurity risks as they become exposed to more dangers. Because they are not dynamic, do not use signatures or only react to threats such traditional cybersecurity methods do not fulfill all the standards required by ICNs. This text focuses on presenting an AICF which works to defend ICNs from evolving cyber risks. The suggested approach combines machine learning (ML) with deep learning (DL) to set up various levels of defense. It features anomaly detection in real time by using unsupervised models, thoroughly sorts threats using deep neural networks and performs tasks automatically to prevent them by using reinforcement learning. Standard datasets created for the industrial domain such as SWaT and NSL-KDD are used to check the effectiveness of the framework. It is shown by experiment that AICF has a strong ability to detect threats, lessens false positives and identifies and controls threats swiftly with little impact on operations. The use of AICF has special value in catching zero-day attacks, moves across the network and clandestine actions that other systems may not spot. Besides, the ability to grow and change according to specific needs means the system is suitable for any industrial environment and protocols. All in all, the study highlights how AI methods can strengthen the strength, dependability and thoughtfulness of cybersecurity in ICN systems, allowing industrial automation to become safer and more reliable when facing new threats.