# Large Language Model-driven Security Assistant for Internet of Things via Chain-of-Thought

arXiv: https://arxiv.org/abs/2505.06307 · HTML: https://mlsanity.com/paper/2505.06307/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 52 references that are in the map.
- First posted on arXiv 8 May 2025.

## Builds on (most cited first, 19 of 19)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning](https://mlsanity.com/paper/2501.12948/) (2025)
- [Tree of Thoughts: Deliberate Problem Solving with Large Language Models](https://mlsanity.com/paper/2305.10601/) (2023)
- [Federated Learning for Internet of Things: A Comprehensive Survey](https://mlsanity.com/paper/2104.07914/) (2021)
- [Federated Learning for Internet of Things: Recent Advances, Taxonomy, and Open Challenges](https://mlsanity.com/paper/2009.13012/) (2020)
- [Chain of Preference Optimization: Improving Chain-of-Thought Reasoning in LLMs](https://mlsanity.com/paper/2406.09136/) (2024)
- [A Survey of Machine and Deep Learning Methods for Internet of Things (IoT) Security](https://mlsanity.com/paper/1807.11023/) (2018)
- [Revolutionizing Cyber Threat Detection with Large Language Models: A privacy-preserving BERT-based Lightweight Model for IoT/IIoT Devices](https://mlsanity.com/paper/2306.14263/) (2023)
- [Machine Learning in IoT Security: Current Solutions and Future Challenges](https://mlsanity.com/paper/1904.05735/) (2019)
- [When IoT Meet LLMs: Applications and Challenges](https://mlsanity.com/paper/2411.17722/) (2024)
- [Vulnerability of Machine Learning Approaches Applied in IoT-based Smart Grid: A Review](https://mlsanity.com/paper/2308.15736/) (2023)
- [Machine Learning-Enabled IoT Security: Open Issues and Challenges Under Advanced Persistent Threats](https://mlsanity.com/paper/2204.03433/) (2022)
- [Distributed Threat Intelligence at the Edge Devices: A Large Language Model-Driven Approach](https://mlsanity.com/paper/2405.08755/) (2024)
- [ChatIoT: Large Language Model-based Security Assistant for Internet of Things with Retrieval-Augmented Generation](https://mlsanity.com/paper/2502.09896/) (2025)
- [Privacy-Preserving Data in IoT-based Cloud Systems: A Comprehensive Survey with AI Integration](https://mlsanity.com/paper/2401.00794/) (2024)
- [Generative AI for Internet of Things Security: Challenges and Opportunities](https://mlsanity.com/paper/2502.08886/) (2025)

## Built on by (most cited first, 2 of 2)

- [SmartBench: Evaluating LLMs in Smart Homes with Anomalous Device States and Behavioral Contexts](https://mlsanity.com/paper/2603.06636/) (2026)
- [CESBench: Benchmarking Large Language Models on Cryptographic Engineering Security for IoT Devices](https://mlsanity.com/paper/2609.21344/) (2026)

## Abstract (arXiv)

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.