Large Language Model-Driven Security Assistant for Internet of Things via Chain-of-Thought
Mingfei Zeng, Ming hui Xie, Xixi Zheng, Chunhai Li, Chuan Zhang, Liehuang Zhu · IEEE Internet of Things Journal · 2025
The rapid development of Internet of Things (IoT) technology has transformed people’s way of life and has a profound impact on both production and daily activities. However, with the rapid advancement of IoT technology, the security of IoT devices has become an unavoidable issue in both research and applications. Although some efforts have been made to detect or mitigate IoT security vulnerabilities, they often struggle to adapt to the complexity of IoT environments, especially when dealing with dynamic security scenarios. How to automatically, efficiently, and accurately understand these vulnerabilities remains a challenge. To address this, we propose an IoT security assistant driven by a Large Language Model (LLM), which, through the ICoT process, enhances the LLM’s understanding of IoT security vulnerabilities and related threats. The ICoT method we propose aims to enable the LLM to understand security issues by breaking down the various dimensions of security vulnerabilities and generating responses tailored to the user’s specific needs and expertise level. By incorporating ICoT, LLM can gradually analyze and reason through complex security scenarios, resulting in more accurate, in-depth, and personalized security recommendations and solutions. Experimental results show that, compared to methods relying solely on LLMs, our proposed LLM-driven IoT security assistant significantly improves the understanding of IoT security issues and provides personalized solutions based on user identity through the ICoT approach. From the evaluator’s perspective, it performs better across five dimensions.