Application of Deep Reinforcement Learning for Intrusion Detection in Internet of Things: A Systematic Review 

Application of Deep Reinforcement Learning for Intrusion Detection in Internet of Things: A Systematic Review | 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 Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed.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. 27 May 2025 V1 Latest version Share on Application of Deep Reinforcement Learning for Intrusion Detection in Internet of Things: A Systematic Review Authors : Saeid Jamshidi 0000-0003-1612-529X [email protected] , Amin Nikanjam , Wazed Nafi , Foutse Khomh , and Rasoul Rasta Authors Info & Affiliations https://doi.org/10.22541/au.174836360.04077727/v1 Published Internet of Things Version of record Peer review timeline 188 views 271 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract The Internet of Things (IoT) has significantly expanded the digital landscape, interconnecting an unprecedented array of devices, from home appliances to industrial equipment. This growth enhances functionality, e.g., automation, remote monitoring, and control, and introduces substantial security challenges, especially in defending these devices against cyber threats. Intrusion Detection Systems (IDS) are crucial for securing IoT; however, traditional IDS often struggle to adapt to IoT networks' dynamic and evolving nature and threat patterns. A potential solution is using Deep Reinforcement Learning (DRL) to enhance IDS adaptability, enabling them to learn from and react to their operational environment dynamically. This systematic review examines the application of DRL to enhance IDS in IoT settings, covering research from the past ten years. This review underscores the state-of-the-art DRL techniques employed to improve adaptive threat detection and real-time security across IoT domains by analyzing various studies. Our findings demonstrate that DRL significantly enhances IDS capabilities by enabling systems to learn and adapt from their operational environment. This adaptability allows IDS to improve threat detection accuracy and minimize false positives, making them more effective in identifying genuine threats while reducing unnecessary alerts. Additionally, this systematic review identifies critical research gaps and future research directions, emphasizing the necessity for more diverse datasets, enhanced reproducibility, and improved integration with emerging IoT technologies. This review aims to foster the development of dynamic and adaptive IDS solutions essential for protecting IoT networks against sophisticated cyber threats. Supplementary Material File (final_slr.pdf) Download 6.25 MB Information & Authors Information Version history V1 Version 1 27 May 2025 Peer review timeline Published Internet of Things Version of Record 1 May 2025 Published Copyright This work is licensed under a Non Exclusive No Reuse License. Keywords deep reinforcement learning internet of things intrusion detection system reinforcement learning Authors Affiliations Saeid Jamshidi 0000-0003-1612-529X [email protected] SWAT Laboratory 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 Rasoul Rasta Department of Computer Engineering, Science and Research Branch, Islamic Azad University View all articles by this author Metrics & Citations Metrics Article Usage 188 views 271 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Saeid Jamshidi, Amin Nikanjam, Wazed Nafi, et al. Application of Deep Reinforcement Learning for Intrusion Detection in Internet of Things: A Systematic Review . Authorea . 27 May 2025. 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