Edge-Intelligence-Empowered, Unified Authentication and Trust Evaluation for Heterogeneous Beyond 5G Systems

Abstract

Integrating artificial intelligence (AI) with mobile edge computing, edge intelligence (EI) has emerged as a new paradigm for 5G and beyond 5G (B5G) systems. The integration of EI and heterogeneous networks (e.g., mobile and wireless local area networks) also raises new concerns about security and privacy. This article examines two important security aspects of EI-empowered, heterogeneous, B5G networks, that is, authentication and trust-evaluation-based compromised user equipment (UE) detection. Technical challenges are discussed. A new edge-computing-enabled, unified authentication framework is developed, which authenticates UEs consistently via heterogeneous networks with UEs' privacy preserved. A new trust-evaluation-based compromised UE detection method is developed based on reinforcement learning to prevent compromised UEs from launching internal attacks. Case studies show that the new frameworks improve the authentication efficiency and detection rate of compromised UEs, as compared to technologies specified by current 5G standards. The frameworks have great potential to secure B5G in future heterogeneous network environments.