Edge-based IDS for IoT Using Combined ML and Generative-AI Models

Edge-based IDS for IoT Using Combined ML and Generative-AI Models | 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 Edge-based IDS for IoT Using Combined ML and Generative-AI Models Khaled Alanezi, Tarun Annapareddy, Shafiullah Khan, Shivakant Mishra This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6569605/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 06 Dec, 2025 Read the published version in Peer-to-Peer Networking and Applications → Version 1 posted 19 You are reading this latest preprint version Abstract This paper utilizes the concepts of fog computing, machine-learning and generative-AI to build accurate and efficient intrusion detection system IDS for the Internet of Things. It proposes a hybrid approach in which the machine learning classifier for the IDS system is built using both real data and synthetic data. The utilized real data will be information evaluated at the edge of the network and deemed to be non-sensitive and hence can be shipped to the cloud to build the generative-AI model. This model will then be used to generate the synthetic data used to augment the partial data. By doing so the security and privacy of the IoT environment is protected while still build an IDS with accepted accuracy. The evaluation shows that by applying techniques such as the light-weight privacy classification and principal component analysis PCA we are able to achieve good intrusion detection accuracy while minimizing the privacy risks to IoT raw data. IoT Security IDS Edge Computing Machine Learning Generative-AI Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 06 Dec, 2025 Read the published version in Peer-to-Peer Networking and Applications → Version 1 posted Editorial decision: Revision requested 11 Jun, 2025 Reviews received at journal 19 May, 2025 Reviewers agreed at journal 18 May, 2025 Reviews received at journal 18 May, 2025 Reviews received at journal 18 May, 2025 Reviews received at journal 15 May, 2025 Reviewers agreed at journal 15 May, 2025 Reviews received at journal 15 May, 2025 Reviewers agreed at journal 15 May, 2025 Reviews received at journal 14 May, 2025 Reviewers agreed at journal 13 May, 2025 Reviewers agreed at journal 13 May, 2025 Reviewers agreed at journal 13 May, 2025 Reviewers agreed at journal 13 May, 2025 Reviewers agreed at journal 12 May, 2025 Reviewers invited by journal 12 May, 2025 Editor assigned by journal 12 May, 2025 Submission checks completed at journal 06 May, 2025 First submitted to journal 01 May, 2025 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. 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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-6569605","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":452502144,"identity":"c724f1d9-0856-4861-bdea-52b5cd9b8173","order_by":0,"name":"Khaled Alanezi","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABEElEQVRIie3NsWqDUBTG8SOCXUxdj1jMKxwppEOT5lUMQlwuJZNzoHCnQlah0LxC8wZXLnSyugpZ7J7Bbg4NVNN2KKilW4f73+7H+XEBVKp/GCJoAgyA89OTpl97s2A/gRMxPsnyzwTk78R+uCtFFcHYOHtJXutVPrY2KUEVSbBjv5M4F8+UxBl43LwNCGnvxQUjLc4kONhNXPRJjjhoHNgEifbaujBJH3EJrtNHwkoeOcy5dbiqfcrm2zwl/ThAHGQkmy8WHNkEBInFk2Ckaw1xeogds1Vyn2HA8XBprykIdsWyXULT3padBItwV9bRdLaxmPdWv9/MHnPZLtcupqKTfMufz/bWHLpXqVQq1XAfodVdRqEGtwEAAAAASUVORK5CYII=","orcid":"","institution":"abdullah Al Salem University","correspondingAuthor":true,"prefix":"","firstName":"Khaled","middleName":"","lastName":"Alanezi","suffix":""},{"id":452502145,"identity":"4f984ef8-6539-4af2-a76f-19a8c6783f70","order_by":1,"name":"Tarun Annapareddy","email":"","orcid":"","institution":"University of Colorado Boulder","correspondingAuthor":false,"prefix":"","firstName":"Tarun","middleName":"","lastName":"Annapareddy","suffix":""},{"id":452502146,"identity":"62961977-7eea-4b67-a032-2e1d870fec6d","order_by":2,"name":"Shafiullah Khan","email":"","orcid":"","institution":"abdullah Al Salem University","correspondingAuthor":false,"prefix":"","firstName":"Shafiullah","middleName":"","lastName":"Khan","suffix":""},{"id":452502147,"identity":"58bcde6e-5033-42b8-9297-a04744de49e2","order_by":3,"name":"Shivakant Mishra","email":"","orcid":"","institution":"University of Colorado Boulder","correspondingAuthor":false,"prefix":"","firstName":"Shivakant","middleName":"","lastName":"Mishra","suffix":""}],"badges":[],"createdAt":"2025-05-01 06:23:10","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6569605/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6569605/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s12083-025-02174-7","type":"published","date":"2025-12-06T15:57:54+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":97724864,"identity":"57763386-2ef0-4bae-b49d-49c03417848d","added_by":"auto","created_at":"2025-12-08 16:13:49","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1208912,"visible":true,"origin":"","legend":"","description":"","filename":"EdgebasedIDSforIoTSpringerP2P1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6569605/v1_covered_4a6b06f2-f78c-4e0f-98c6-eb19fe7902e3.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Edge-based IDS for IoT Using Combined ML and Generative-AI Models","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"peer-to-peer-networking-and-applications","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ppna","sideBox":"Learn more about [Peer-to-Peer Networking and Applications](http://link.springer.com/journal/12083)","snPcode":"12083","submissionUrl":"https://submission.nature.com/new-submission/12083/3","title":"Peer-to-Peer Networking and Applications","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"IoT Security, IDS, Edge Computing, Machine Learning, Generative-AI","lastPublishedDoi":"10.21203/rs.3.rs-6569605/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6569605/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"This paper utilizes the concepts of fog computing, machine-learning and generative-AI to build accurate and efficient intrusion detection system IDS for the Internet of Things. 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