Large language models (LLMs), especially generative pre-trained transformers (GPTs), have recently demonstrated outstanding ability in information comprehension and problem-solving. This has motivated many studies in applying LLMs to wireless communication networks. In this paper, we propose a pre-trained LLM-empowered framework to perform fully automatic network intrusion detection. Three in-context learning methods are designed and compared to enhance the performance of LLMs. With experiments on a real network intrusion detection dataset, in-context learning proves to be highly beneficial in improving the task processing performance in a way that no further training or fine-tuning of LLMs is required. We show that for GPT-4, testing accuracy and F1-Score can be improved by 90%. Moreover, pre-trained LLMs demonstrate big potential in performing wireless communication-related tasks. Specifically, the proposed framework can reach an accuracy and F1-Score of over 95% on different types of attacks with GPT-4 using only 10 in-context learning examples.
@article{arxiv.2405.11002,
title = {Large Language Models in Wireless Application Design: In-Context Learning-enhanced Automatic Network Intrusion Detection},
author = {Han Zhang and Akram Bin Sediq and Ali Afana and Melike Erol-Kantarci},
journal= {arXiv preprint arXiv:2405.11002},
year = {2024}
}