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

A Survey on XAI for Beyond 5G Security: Technical Aspects, Use Cases, Challenges and Research Directions