Asynchronous federated learning based zero trust architecture for the next generation industrial control systems

Abstract

The zero-trust architecture (ZTA) is an emerging technology for ensuring the security of next-generation industrial control systems (ICSs). However, ICSs are complex and characterised by diverse equipment, cyber-physical integration, dynamic network topologies and stringent real-time demands, which present significant challenges to ZTA implementation. Moreover, as enterprises increasingly share data to identify advanced business patterns, the risk of data breaches escalates during the digitalisation and intelligent transformation process. To address these issues, this article proposes a ZTA for next-generation ICSs based on asynchronous federated deep learning (FDL). Both physical and cyber information is considered in trust evaluations, except for subject and object attributes. This can significantly enhance the accuracy of zero-trust decision-making. Furthermore, a novel grouping-based asynchronous federated learning algorithm is proposed to reduce the aggregation delay experienced by different devices, grouping those with similar computing capabilities and business urgency requirements. Additionally, optimising model aggregation enhances the model’s adaptability to swift changes in ICSs environments. Through rigorous validation in a real gas pipeline system in our laboratory, we demonstrated the effectiveness of our proposed ZTA, showing that it is superior to alternative methodologies.