AI-Based Secure Routing: Intrusion Detection for IoT Networks

AI-Based Secure Routing: Intrusion Detection for IoT Networks | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article AI-Based Secure Routing: Intrusion Detection for IoT Networks R Leelavathi, A Vidya This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6284159/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Security threats in IoT networks, particularly routing attacks such as Sybil, sinkhole, blackhole, and wormhole attacks, compromise data integrity and network performance. To mitigate these threats, we propose AIRS (AI-Driven Intrusion-Resilient Secure Routing), an AI-powered secure routing algorithm that integrates machine learning-based anomaly detection with a trust-aware routing mechanism to enhance network resilience. AIRS was implemented in the Cooja simulator and evaluated against Secure-RPL and Trust-Based LEACH. Simulation results show that AIRS achieves 96.5% intrusion detection accuracy, improves packet delivery ratio (94%), and reduces energy consumption (1.3J per node), leading to a 40% increase in network lifetime compared to Secure-RPL. Additionally, AIRS minimizes false alarms, achieving a false positive rate of 3.5%, reducing unnecessary security overhead. The results demonstrate that AI-driven intrusion detection enhances routing security and efficiency in IoT networks. AIRS provides a scalable, energy-efficient, and resilient security solution for large-scale IoT deployments. Future work will focus on federated learning-based adaptive detection and real-world implementation to further validate AIRS in dynamic IoT environments. IoT Security Intrusion Detection AI-Driven Routing Secure Routing Machine Learning Energy-Efficient Routing Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6284159","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":433229120,"identity":"a7876a16-a0a4-45cd-b868-2d48e47f2eba","order_by":0,"name":"R Leelavathi","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABAElEQVRIie3PsUoDMRjA8YSDdlFu/QoFX6Eu2qHig7h84Vaz33CWTNelcGsLUl+hLjfnCKRLoA/QJVJwdrxFMXFpHe6yCuY/JBfy/QhHSCz2N2OSkKn/GBDM3ZYkIkTQEfCjjhi/0yAhJ0JLfw6Q24W08iOHq8lur+3b5ukhXTjS5nUnGRvEZmXgemuy4YTVO75SVNClOXQSIIjqsgS6FtkAWK25cCShZQ9JLarPL7hfV0dHnjV/CRJwr1ABrAL/iij4NkwsNksNWQXHG0At+asjTe+/pI/MtsXsrkzZ+6gt5nyzV41t825CyAWen9TPKnvmXcNf9/P+4VgsFvuXfQMl9V684EnxygAAAABJRU5ErkJggg==","orcid":"","institution":"Vivekananda Institute of Technology","correspondingAuthor":true,"prefix":"","firstName":"R","middleName":"","lastName":"Leelavathi","suffix":""},{"id":433229122,"identity":"93ee5d28-2e56-4eb9-9f75-6167dbd62e31","order_by":1,"name":"A Vidya","email":"","orcid":"","institution":"Vivekananda Institute of Technology","correspondingAuthor":false,"prefix":"","firstName":"A","middleName":"","lastName":"Vidya","suffix":""}],"badges":[],"createdAt":"2025-03-22 14:23:17","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6284159/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6284159/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":79147762,"identity":"1bd28468-6685-46db-8c67-4d622f4794e6","added_by":"auto","created_at":"2025-03-25 03:34:08","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":408696,"visible":true,"origin":"","legend":"","description":"","filename":"AIRSV1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6284159/v1_covered_8fbb1b53-15ad-4561-a9ea-674cdc49c09d.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"AI-Based Secure Routing: Intrusion Detection for IoT Networks","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"IoT Security, Intrusion Detection, AI-Driven Routing, Secure Routing, Machine Learning, Energy-Efficient Routing","lastPublishedDoi":"10.21203/rs.3.rs-6284159/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6284159/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eSecurity threats in IoT networks, particularly routing attacks such as Sybil, sinkhole, blackhole, and wormhole attacks, compromise data integrity and network performance. 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