Aug 05, 2025
DOI: 10.1109/ACCESS.2025.3595665
Published in: IEEE Access
Publisher: IEEE
Sixth Generation (6G) wireless networks, which are expected to be deployed in the 2030s, have already created great excitement in academia and the private sector with their extremely high communication speed and low latency rates. However, despite the ultra-low latency, high throughput, and AI-assisted orchestration capabilities they promise, they are vulnerable to stealthy and long-term Advanced Persistent Threats (APTs). Large Language Models (LLMs) stand out as an ideal candidate to fill this gap with their high success in semantic reasoning and threat intelligence. This paper presents the first systematic review and taxonomy for LLM-assisted APT detection in 6G networks. It also provides insights by reviewing recent research on the intersection of LLMs, APTs, and 6G. Key challenges such as limitations in edge deployment, data scarcities, and explainability gaps are identified and a multidimensional taxonomy is provided in line with the APT lifecycle and 6G contexts. The paper is based on 142 studies from 2018 to 2025, searching leading databases such as IEEE Xplore, ACM Digital Library, SpringerLink, and Elsevier ScienceDirect.
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