AI-Based Autonomic and Scalable Security Management Architecture for Secure Network Slicing in B5G
Chafika Benzaïd, Tarik Taleb, JaeSeung Song
Abstract
The vital importance of securing 5G and beyond networks while meeting their stringent performance requirements has promoted the recent shift towards fully automated and smart security management. In this article, we introduce a novel autonomic and cognitive security management framework that empowers fine-grained zero-touch security management through different levels (i.e., network functions, sub-slice, and slice) and different administrative and technological domains. We showcase the compliance of the proposed framework with the ongoing standards (e.g., ZSM, 3GPP, and NFV) and demonstrate its feasibility by advocating for potential open source solutions to implement its functional blocks in a cloud-native service-based environment.