Large Language Model-driven Security Assistant for Internet of Things via Chain-of-Thought
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arXiv
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| Auteurs principaux: | , , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866909606298517504 |
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| author | Zeng, Mingfei Xie, Ming Zheng, Xixi Li, Chunhai Zhang, Chuan Zhu, Liehuang |
| author_facet | Zeng, Mingfei Xie, Ming Zheng, Xixi Li, Chunhai Zhang, Chuan Zhu, Liehuang |
| contents | 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 Large Language Model (LLM), which enhances the LLM's understanding of IoT security vulnerabilities and related threats. The aim of the ICoT method we propose is 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 LLM, our proposed LLM-driven IoT security assistant significantly improves the understanding of IoT security issues through the ICoT approach and provides personalized solutions based on the user's identity, demonstrating higher accuracy and reliability. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_06307 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Large Language Model-driven Security Assistant for Internet of Things via Chain-of-Thought Zeng, Mingfei Xie, Ming Zheng, Xixi Li, Chunhai Zhang, Chuan Zhu, Liehuang Cryptography and Security Artificial Intelligence 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 Large Language Model (LLM), which enhances the LLM's understanding of IoT security vulnerabilities and related threats. The aim of the ICoT method we propose is 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 LLM, our proposed LLM-driven IoT security assistant significantly improves the understanding of IoT security issues through the ICoT approach and provides personalized solutions based on the user's identity, demonstrating higher accuracy and reliability. |
| title | Large Language Model-driven Security Assistant for Internet of Things via Chain-of-Thought |
| topic | Cryptography and Security Artificial Intelligence |
| url | https://arxiv.org/abs/2505.06307 |