A Cyber Range Framework for Emulating Secure and Private IT/OT Consumer Service Verticals Toward 6G

In the era of 6G technology, the convergence of Information Technology (IT) and Operational Technology (OT) is revolutionizing the way digital twin architectures are deployed for consumer applications. This convergence of IT and OT can leverage edge learning to transform 3D metaverse applications, making them more accessible and intelligent for Consumer Electronics (CE) users. In order to demonstrate IT/OT convergence and enable seamless, instantaneous interactions between physical entities and their digital counterparts in the CE context, we proposed a Digital Twin Metaverse Network (DTMN) that processes data from a wide range of CE applications and endpoints at the network’s edge. To strengthen the cyber resilience of consumer applications, we also propose an innovative cyber-range model that leverages deep learning techniques and is based on the MITRE attack and defense architecture. It addresses the paramount challenges of data security and privacy in a network that hosts CE applications and endpoints. Through known attack and defense scenarios, we showcase our model’s effectiveness in protecting consumer data and ensuring the reliability of CE applications within the DTMN ecosystem. The obtained results show how AI-assisted edge learning can improve the security and functionality of consumer applications, advancing towards a secure and interconnected digital future.