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Main Authors: Khatiwada, Kushal, Hopper, Jayden, Cheatham, Joseph, Joshi, Ayan, Baidya, Sabur
Format: Preprint
Published: 2025
Subjects:
Online Access:https://arxiv.org/abs/2505.17586
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author Khatiwada, Kushal
Hopper, Jayden
Cheatham, Joseph
Joshi, Ayan
Baidya, Sabur
author_facet Khatiwada, Kushal
Hopper, Jayden
Cheatham, Joseph
Joshi, Ayan
Baidya, Sabur
contents The Internet of Things (IoT) and Large Language Models (LLMs) have been two major emerging players in the information technology era. Although there has been significant coverage of their individual capabilities, our literature survey sheds some light on the integration and interaction of LLMs and IoT devices - a mutualistic relationship in which both parties leverage the capabilities of the other. LLMs like OpenAI's ChatGPT, Anthropic's Claude, Google's Gemini/BERT, any many more, all demonstrate powerful capabilities in natural language understanding and generation, enabling more intuitive and context-aware interactions across diverse IoT applications such as smart cities, healthcare systems, industrial automation, and smart home environments. Despite these opportunities, integrating these resource-intensive LLMs into IoT devices that lack the state-of-the-art computational power is a challenging task. The security of these edge devices is another major concern as they can easily act as a backdoor to private networks if the LLM integration is sloppy and unsecured. This literature survey systematically explores the current state-of-the-art in applying LLMs within IoT, emphasizing their applications in various domains/sectors of society, the significant role they play in enhancing IoT security through anomaly detection and threat mitigation, and strategies for effective deployment using edge computing frameworks. Finally, this survey highlights existing challenges, identifies future research directions, and underscores the need for cross-disciplinary collaboration to fully realize the transformative potential of integrating LLMs and IoT.
format Preprint
id arxiv_https___arxiv_org_abs_2505_17586
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle Large Language Models in the IoT Ecosystem -- A Survey on Security Challenges and Applications
Khatiwada, Kushal
Hopper, Jayden
Cheatham, Joseph
Joshi, Ayan
Baidya, Sabur
Cryptography and Security
The Internet of Things (IoT) and Large Language Models (LLMs) have been two major emerging players in the information technology era. Although there has been significant coverage of their individual capabilities, our literature survey sheds some light on the integration and interaction of LLMs and IoT devices - a mutualistic relationship in which both parties leverage the capabilities of the other. LLMs like OpenAI's ChatGPT, Anthropic's Claude, Google's Gemini/BERT, any many more, all demonstrate powerful capabilities in natural language understanding and generation, enabling more intuitive and context-aware interactions across diverse IoT applications such as smart cities, healthcare systems, industrial automation, and smart home environments. Despite these opportunities, integrating these resource-intensive LLMs into IoT devices that lack the state-of-the-art computational power is a challenging task. The security of these edge devices is another major concern as they can easily act as a backdoor to private networks if the LLM integration is sloppy and unsecured. This literature survey systematically explores the current state-of-the-art in applying LLMs within IoT, emphasizing their applications in various domains/sectors of society, the significant role they play in enhancing IoT security through anomaly detection and threat mitigation, and strategies for effective deployment using edge computing frameworks. Finally, this survey highlights existing challenges, identifies future research directions, and underscores the need for cross-disciplinary collaboration to fully realize the transformative potential of integrating LLMs and IoT.
title Large Language Models in the IoT Ecosystem -- A Survey on Security Challenges and Applications
topic Cryptography and Security
url https://arxiv.org/abs/2505.17586