Abstract
The rapid advancement of Large Language Models (LLMs) has opened up new opportunities for leveraging artificial intelligence in various domains, including cybersecurity. As the volume and sophistication of cyber threats continue to grow, there is an increasing need for intelligent systems that can automatically detect vulnerabilities, analyze malware, and respond to attacks. In this survey, we conduct a comprehensive review of the literature on the application of LLMs in cybersecurity (LLM4Security). By comprehensively collecting over 30K relevant papers and systematically analyzing 127 papers from top security and software engineering venues, we aim to provide a holistic view of how LLMs are being used to solve diverse problems across the cybersecurity domain. Through our analysis, we identify several key findings. First, we observe that LLMs are being applied to a wide range of cybersecurity tasks, including vulnerability detection, malware analysis, network intrusion detection, and phishing detection. Second, we find that the datasets used for training and evaluating LLMs in these tasks are often limited in size and diversity, highlighting the need for more comprehensive and representative datasets. Third, we identify several promising techniques for adapting LLMs to specific cybersecurity domains, such as fine-tuning, transfer learning, and domain-specific pre-training. Finally, we discuss the main challenges and opportunities for future research in LLM4Security, including the need for more interpretable and explainable models, the importance of addressing data privacy and security concerns, and the potential for leveraging LLMs for proactive defense and threat hunting. Overall, our survey provides a comprehensive overview of the current state-of-the-art in LLM4Security and identifies several promising directions for future research.
Abstract (translated)
大规模语言模型的快速发展为利用人工智能在各个领域提供了新的机会，包括网络安全。随着网络威胁的数量和复杂性不断增加，人们越来越需要能够自动检测漏洞、分析恶意软件并应对攻击的智能系统。在本次调查中，我们对大规模语言模型在网络安全领域的应用进行全面回顾（LLM4Security）。通过全面收集超过3000篇相关论文并系统分析来自顶级安全和水下工程领域的127篇论文，我们希望为读者提供全面了解大规模语言模型在网络安全领域应用的视角。通过我们的分析，我们发现了几个关键发现。首先，我们观察到大规模语言模型被应用于广泛的网络安全任务，包括漏洞检测、恶意软件分析、网络入侵检测和网络钓鱼检测。其次，我们发现用于训练和评估大规模语言模型在这些任务中所用的数据集往往规模有限且多样性不足，强调了对更全面和代表性的数据集的需求。第三，我们识别出几种将大规模语言模型适应特定网络安全领域的有前途的方法，例如微调、迁移学习和领域特定的预训练。最后，我们讨论了在LLM4Security领域未来研究的主要挑战和机遇，包括需要更可解释和可解释的模型、解决数据隐私和安全问题的迫切需要以及利用大规模语言模型进行主动防御和威胁狩猎的可能性。总的来说，我们的调查为LLM4Security领域提供了全面回顾，并提出了几个有前途的研究方向。
URL
https://arxiv.org/abs/2405.04760
