AI-based autonomic & scalable security management architecture for secure network slicing in B5G
Benzaïd, Chafika; Taleb, Tarik; Song, JaeSeung (2022-07-25)
C. Benzaïd, T. Taleb and J. Song, "AI-Based Autonomic and Scalable Security Management Architecture for Secure Network Slicing in B5G," in IEEE Network, vol. 36, no. 6, pp. 165-174, November/December 2022, doi: 10.1109/MNET.104.2100495
© 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
https://rightsstatements.org/vocab/InC/1.0/
https://urn.fi/URN:NBN:fi-fe2022092660156
Tiivistelmä
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 paper, 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.
Kokoelmat
- Avoin saatavuus [31928]