  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.
