A Deep Learning Assisted Software Defined Security Architecture for 6G Wireless Networks: IIoT Perspective

Abstract

The 6G wireless network is expected to drive cyber-physical systems (CPS) from merely connected things to securely connected intelligence. While 6G will offer real-time communication between cyber and physical entities, due to convergence of operational technology (OT) and information technology (IT) networks, security, and trustworthiness of the massive amount of data shared between cyber and physical entities will remain of great concern. Attackers having AI capability will be able to mount massive numbers of automated and novel attacks on the future 6G network. Human security specialists teaming with an AI-powered adaptive defense mechanism will be needed to counter emerging AI-based attacks on the massively connected CPS through 6G wireless networks. 6G networks are expected to add industrial immunity to IT, OT, and IIoT networks with the help of AI. 6G is expected to offer deep learning (DL) assisted security function virtualization (SFV) to support software defined security (SDS) architecture for dynamic defense mechanisms, intelligently monitor network traffic anomalies at different network endpoints and segments, and offer increased visibility across attack surfaces. In this article, we study the security challenges in 6G networks posed by the recent convergence of OT and IT networks and propose distributed DL-assisted SDS for 6G vertical that will autonomously detect, localize, and isolate security threats via SFV. Finally, we present future directions and the challenges ahead.