The rapid evolution of cyber threats necessitates innovative solutions for detecting and analyzing malicious activity. Honeypots, which are decoy systems designed to lure and interact with attackers, have emerged as a critical component in cybersecurity. In this paper, we present a novel approach to creating realistic and interactive honeypot systems using Large Language Models (LLMs). By fine-tuning a pre-trained open-source language model on a diverse dataset of attacker-generated commands and responses, we developed a honeypot capable of sophisticated engagement with attackers. Our methodology involved several key steps: data collection and processing, prompt engineering, model selection, and supervised fine-tuning to optimize the model's performance. Evaluation through similarity metrics and live deployment demonstrated that our approach effectively generates accurate and informative responses. The results highlight the potential of LLMs to revolutionize honeypot technology, providing cybersecurity professionals with a powerful tool to detect and analyze malicious activity, thereby enhancing overall security infrastructure.

本研究针对网络安全领域中现有蜜罐系统的局限性，提出了一种基于大型语言模型的实时交互蜜罐新方法。通过对攻击者生成的指令和反应进行数据处理及模型微调，我们的新型蜜罐能够与攻击者进行复杂的互动，从而更有效地检测和分析恶意活动，显著提升了网络安全防护能力。

大型语言模型蜜罐：利用大型语言模型作为先进的交互式蜜罐系统