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The Role of Large Language Models in IoT Security: A Systematic Review of Advances, Challenges, and Opportunities ⋆,⋆⋆ | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 12 August 2025 V1 Latest version Share on The Role of Large Language Models in IoT Security: A Systematic Review of Advances, Challenges, and Opportunities ⋆,⋆⋆ Authors : Saeid Jamshidi 0000-0003-1612-529X [email protected] , Negar Shahabi , Amin Nikanjam , Wazed Nafi , Foutse Khomh , and Carol Fung Authors Info & Affiliations https://doi.org/10.22541/au.175501749.91343297/v1 Published Internet of Things Version of record Peer review timeline 277 views 241 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract The Internet of Things (IoT) has revolutionized digital ecosystems by interconnecting billions of devices across various industries, enabling enhanced automation, real-time monitoring, and data-driven decision-making. However, this expansion has introduced significant security and privacy challenges due to the heterogeneous nature of IoT devices, resource constraints, and the decentralized nature of their architectures. Large Language Models (LLMs) have recently shown promise in improving cybersecurity by enabling automated threat intelligence, anomaly detection, malware classification, and privacy-aware security enforcement. Therefore, this systematic review investigates research published between 2015 and 2025 to examine the intersection of LLMs, IoT security, and privacy. We evaluate state-of-the-art LLM-based security frameworks, highlighting their effectiveness, limitations, and impact on IoT cybersecurity. In addition, this review identifies key research gaps and challenges, providing insight into the scalability, efficiency, and adaptability of LLM-driven security solutions. This work aims to contribute to the advancement of AI-driven IoT security frameworks, supporting the development of resilient and privacy-preserving cybersecurity architectures. Supplementary Material File (iot_llm_slr.pdf) Download 7.36 MB Information & Authors Information Version history V1 Version 1 12 August 2025 Peer review timeline Published Internet of Things Version of Record 1 Nov 2025 Published Copyright This work is licensed under a Non Exclusive No Reuse License. Keywords cybersecurity internet of things large language models (llms) privacy security Authors Affiliations Saeid Jamshidi 0000-0003-1612-529X [email protected] SWAT Laboratory View all articles by this author Negar Shahabi Concordia Institute for Information Systems Engineering (CIISE), Concordia University View all articles by this author Amin Nikanjam SWAT Laboratory View all articles by this author Wazed Nafi SWAT Laboratory View all articles by this author Foutse Khomh SWAT Laboratory View all articles by this author Carol Fung Concordia Institute for Information Systems Engineering (CIISE), Concordia University View all articles by this author Metrics & Citations Metrics Article Usage 277 views 241 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Saeid Jamshidi, Negar Shahabi, Amin Nikanjam, et al. The Role of Large Language Models in IoT Security: A Systematic Review of Advances, Challenges, and Opportunities ⋆,⋆⋆. Authorea . 12 August 2025. DOI: https://doi.org/10.22541/au.175501749.91343297/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. For more information or tips please see 'Downloading to a citation manager' in the Help menu . Format Please select one from the list RIS (ProCite, Reference Manager) EndNote BibTex Medlars RefWorks Direct import Tips for downloading citations document.getElementById('citMgrHelpLink').addEventListener('click', function() { popupHelp(this.href); return false; }); $(".js__slcInclude").on("change", function(e){ if ($(this).val() == 'refworks') $('#direct').prop("checked", false); $('#direct').prop("disabled", ($(this).val() == 'refworks')); }); View Options View options PDF View PDF Figures Tables Media Share Share Share article link Copy Link Copied! Copying failed. 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