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

AI systems protecting 5G networks need transparency, not just accuracy: The paper emphasizes that current security surveys focus too heavily on how well ML models perform rather than whether stakeholders can understand and trust their decisions. XAI methods are proposed as essential to make these "black-box" security systems accountable and comprehensible to network operators and users.

Explainability is critical for automated security decisions: As 5G and beyond networks increasingly rely on AI to make automated security decisions, XAI becomes necessary so that stakeholders can inspect why the system took a particular action—whether blocking traffic, flagging threats, or managing network access—rather than simply accepting its output.

XAI applies across the entire next-generation network infrastructure: The paper identifies XAI's relevance across multiple 5G/B5G technologies including ORAN (Open RAN), zero-touch network management, and end-to-end network slicing, indicating that explainability concerns span from edge devices to cloud-based systems, not just isolated components.

Trustworthiness and accountability must be built into network security from the start: The survey highlights that as telecommunications systems become more intelligent and autonomous, the ability to audit and understand AI-driven security decisions becomes a fundamental requirement for responsible deployment, not an optional add-on.