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.

翻译：大型语言模型（LLMs），特别是生成式预训练变换器（GPTs），近期在信息理解与问题解决方面展现出卓越能力。这推动了将LLMs应用于无线通信网络的多项研究。本文提出一种基于预训练LLM的框架，用于实现全自动网络入侵检测。我们设计并比较了三种情境学习方法以提升LLMs性能。基于真实网络入侵检测数据集的实验表明，情境学习能显著提升任务处理性能，且无需对LLMs进行额外训练或微调。结果显示，对于GPT-4，测试准确率与F1分数可提升90%。此外，预训练LLMs在处理无线通信相关任务中展现出巨大潜力。具体而言，所提框架在使用GPT-4且仅需10个情境学习示例的条件下，对不同类型攻击的检测准确率与F1分数均超过95%。