测试大模型在5G安全威胁建模中的适用性，发现需微调才能用。
LLMs' Suitability for Network Security: A Case Study of STRIDE Threat Modeling
- 用四种提示法+五种大模型对5G威胁做STRIDE分类
- 模型在部分威胁上准确率不足，暴露出适配缺陷
- 适合安全研究者参考，尤其关注模型定制化方向
人工智能（AI）预计将成为下一代AI原生6G网络的重要组成部分。随着AI的普及，研究人员已识别出众多其在网络安全中的应用场景。然而，针对大语言模型（LLMs）在网络安全中适用性的研究仍非常有限。为填补这一空白，本文聚焦于大语言模型在网络安全中的适用性，以STRIDE威胁建模为例展开研究。我们采用四种提示技术与五种主流大语言模型，对5G网络威胁进行STRIDE分类。评估结果揭示了若干关键发现与深入见解，并分析了影响大模型在特定威胁建模中表现的潜在因素。数值结果与分析表明，为满足网络安全应用需求，必须对大模型进行调整与微调。
原文摘要 · Abstract (English)
Artificial Intelligence (AI) is expected to be an integral part of next-generation AI-native 6G networks. With the prevalence of AI, researchers have identified numerous use cases of AI in network security. However, there are very few studies that analyze the suitability of Large Language Models (LLMs) in network security. To fill this gap, we examine the suitability of LLMs in network security, particularly with the case study of STRIDE threat modeling. We utilize four prompting techniques with five LLMs to perform STRIDE classification of 5G threats. From our evaluation results, we point out key findings and detailed insights along with the explanation of the possible underlying factors influencing the behavior of LLMs in the modeling of certain threats. The numerical results and the insights support the necessity for adjusting and fine-tuning LLMs for network security use cases.
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