Large Language Models in Wireless Application Design: In-Context Learning-enhanced Automatic Network Intrusion Detection

• URL: http://arxiv.org/abs/2405.11002v1
• Date: Fri, 17 May 2024 02:56:31 GMT
• Title: Large Language Models in Wireless Application Design: In-Context Learning-enhanced Automatic Network Intrusion Detection
• Authors: Han Zhang, Akram Bin Sediq, Ali Afana, Melike Erol-Kantarci,
• Abstract summary: We propose a pre-trained LLM-empowered framework to perform fully automatic network intrusion detection. With experiments on a real network intrusion detection dataset, in-context learning proves to be highly beneficial. We show that for GPT-4, testing accuracy and F1-Score can be improved by 90%.
• Score: 11.509880721677156
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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