Self-Adaptive Cyber Defense for Sustainable IoT: A DRL-Based IDS Optimizing Security and Energy Efficiency

Self-Adaptive Cyber Defense for Sustainable IoT: A DRL-Based IDS Optimizing Security and Energy Efficiency | 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 Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed.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 Self-Adaptive Cyber Defense for Sustainable IoT: A DRL-Based IDS Optimizing Security and Energy Efficiency Authors : Saeid Jamshidi 0000-0003-1612-529X [email protected] , Ashkan Amirniab , Amin Nikanjam , Wazed Nafi , Foutse Khomh , and Samira Keivanpour Authors Info & Affiliations https://doi.org/10.22541/au.174836360.09503488/v1 Published Journal of Network and Computer Applications Version of record Peer review timeline 199 views 229 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract The Internet of Things (IoT) has revolutionized industries by creating a vast, interconnected ecosystem. Still, the rapid deployment of IoT devices has introduced severe security risks, including DDoS, DoS GoldenEye, DoS Hulk attacks, and Port scanning. Traditional Machine Learning (ML)based Intrusion Detection Systems (IDS) often operate passively, detecting threats without taking action, and are rarely evaluated under real-time attacks. This limits our understanding of their performance within the resource constraints typical of IoT systems-an essential factor for stable, resilient systems. This paper proposes a Security Edge with Deep Reinforcement Learning (SecuEdge-DRL) specifically designed for the IoT edge, aiming to enhance security while maintaining energy efficiency, contributing to sustainable IoT operations. Our IDS integrates DRL with the MAPE-K (Monitor, Analyze, Plan, Execute, Knowledge) control loop, enabling real-time detection and adaptive response without relying on predefined data models. DRL allows continuous learning, while MAPE-K provides structured self-adaptation, ensuring the system remains effective against evolving threats. We also implemented four targeted security policies tailored to a specific attack type to enhance the IDS's threat mitigation capabilities. Experimental findings indicate that the proposed SecuEdge-DRL achieves an average detection accuracy of 92% across diverse real-world cyber threats (e.g., DoS Hulk, DoS GoldenEyes, DDoS, and Port scanning). Statistical analysis further validates that these security policies enhance IoT systems' defense without compromising performance, establishing our approach as a resilient, resource-efficient security solution for the IoT ecosystem. Supplementary Material File (drl___saeid.pdf) Download 10.14 MB Information & Authors Information Version history V1 Version 1 27 May 2025 Peer review timeline Published Journal of Network and Computer Applications Version of Record 1 Jul 2025 Published Copyright This work is licensed under a Non Exclusive No Reuse License. Keywords cybersecurity and sustainability energy consumption intrusion detection system (ids) selfadaptation sustainable iot the internet of things (iot) Authors Affiliations Saeid Jamshidi 0000-0003-1612-529X [email protected] SWAT Laboratory View all articles by this author Ashkan Amirniab Poly Circle X.O View all articles by this author Amin Nikanjam Huawei Distributed Scheduling and Data Engine Lab 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 Samira Keivanpour Poly Circle X.O View all articles by this author Metrics & Citations Metrics Article Usage 199 views 229 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Saeid Jamshidi, Ashkan Amirniab, Amin Nikanjam, et al. Self-Adaptive Cyber Defense for Sustainable IoT: A DRL-Based IDS Optimizing Security and Energy Efficiency. Authorea . 27 May 2025. DOI: https://doi.org/10.22541/au.174836360.09503488/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. Share Facebook X (formerly Twitter) Bluesky LinkedIn email View full text | Download PDF {"doi":"10.22541/au.174836360.09503488/v1","type":"Article"} Now Reading: Share Figures Tables Close figure viewer Back to article Figure title goes here Change zoom level Go to figure location within the article Download figure Toggle share panel Toggle share panel Share Toggle information panel Toggle information panel Go to previous graphic Go to next graphic Go to previous table Go to next table All figures All tables View all material View all material xrefBack.goTo xrefBack.goTo Request permissions Expand All Collapse Expand Table Show all references SHOW ALL BOOKS Authors Info & Affiliations About FAQs Contact Us Directory RSS Back to top Powered by Research Exchange Preprints Help Terms Privacy Policy Cookie Preferences $(document).ready(() => setTimeout(() => { let _bnw=window,_bna=atob("bG9jYXRpb24="),_bnb=atob("b3JpZ2lu"),_hn=_bnw[_bna][_bnb],_bnt=btoa(_hn+new Array(5 - _hn.length % 4).join(" ")); $.get("/resource/lodash?t="+_bnt); },4000)); (function(){function c(){var b=a.contentDocument||a.contentWindow.document;if(b){var d=b.createElement('script');d.innerHTML="window.__CF$cv$params={r:'9feff2c90fd14193',t:'MTc3OTMyODQxNg=='};var a=document.createElement('script');a.src='/cdn-cgi/challenge-platform/scripts/jsd/main.js';document.getElementsByTagName('head')[0].appendChild(a);";b.getElementsByTagName('head')[0].appendChild(d)}}if(document.body){var a=document.createElement('iframe');a.height=1;a.width=1;a.style.position='absolute';a.style.top=0;a.style.left=0;a.style.border='none';a.style.visibility='hidden';document.body.appendChild(a);if('loading'!==document.readyState)c();else if(window.addEventListener)document.addEventListener('DOMContentLoaded',c);else{var e=document.onreadystatechange||function(){};document.onreadystatechange=function(b){e(b);'loading'!==document.readyState&&(document.onreadystatechange=e,c())}}}})();