LLM-Driven APT Detection for 6G Wireless Networks: A Systematic Review and Taxonomy

Document Type

Article

Source of Publication

IEEE Access

Publication Date

1-1-2025

Abstract

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.

ISSN

2169-3536

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Volume

13

First Page

145271

Last Page

145288

Disciplines

Computer Sciences

Keywords

6G wireless networks, advanced persistent threat (APT), large language model (LLM), natural language processing (NLP), security

Scopus ID

105013128178

Creative Commons License

Creative Commons Attribution 4.0 International License
This work is licensed under a Creative Commons Attribution 4.0 International License.

Indexed in Scopus

yes

Open Access

yes

Open Access Type

Gold: This publication is openly available in an open access journal/series

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