With the advent of 5G commercialization, the need for more reliable, faster, and intelligent telecommunication systems are envisaged for the next generation beyond 5G (B5G) radio access technologies. Artificial Intelligence (AI) and Machine Learning (ML) are not just immensely popular in the service layer applications but also have been proposed as essential enablers in many aspects of B5G networks, from IoT devices and edge computing to cloud-based infrastructures. However, existing B5G ML-security surveys tend to place more emphasis on AI/ML model performance and accuracy than on the models' accountability and trustworthiness. In contrast, this paper explores the potential of Explainable AI (XAI) methods, which would allow B5G stakeholders to inspect intelligent black-box systems used to secure B5G networks. The goal of using XAI in the security domain of B5G is to allow the decision-making processes of the ML-based security systems to be transparent and comprehensible to B5G stakeholders making the systems accountable for automated actions. In every facet of the forthcoming B5G era, including B5G technologies such as RAN, zero-touch network management, E2E slicing, this survey emphasizes the role of XAI in them and the use cases that the general users would ultimately enjoy. Furthermore, we presented the lessons learned from recent efforts and future research directions on top of the currently conducted projects involving XAI.

翻译：随着5G商业化进程的推进，业界预期下一代超越5G（B5G）无线接入技术将需要更可靠、更快速且更智能的电信系统。人工智能（AI）与机器学习（ML）不仅在服务层应用中广受欢迎，还被视为B5G网络从物联网设备、边缘计算到云基础设施等多个方面的关键赋能技术。然而，现有的B5G机器学习安全综述更侧重于AI/ML模型的性能与准确性，而非模型的问责性与可信度。相比之下，本文探讨了可解释人工智能（XAI）方法的潜力，这些方法能使B5G利益相关方审视用于保护B5G网络的智能黑盒系统。在B5G安全领域应用XAI的目标，在于使基于ML的安全系统的决策过程对B5G利益相关方透明且可理解，从而让系统对其自动化行为负责。针对即将到来的B5G时代的各个层面，包括无线接入网（RAN）、零接触网络管理、端到端（E2E）切片等B5G技术，本综述重点阐述了XAI在其中扮演的角色以及最终用户能够享受的应用案例。此外，我们基于当前正在进行中的XAI相关项目，总结了近期研究成果的经验教训，并提出了未来的研究方向。