IOT edge computing layer modification based cyber-attack detection using Federated-Active Learning

IOT edge computing layer modification based cyber-attack detection using Federated-Active Learning | 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 IOT edge computing layer modification based cyber-attack detection using Federated-Active Learning J Vinothini, Srie Vidhya Janani E This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5281086/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 The Internet of Things and its practical uses are becoming more widespread as the number of connected devices increases, but it always carries a risk to network security. Therefore, it is vital for an IoT network design to rapidly and accurately identify potential attackers. While many proposed solutions focus on secure IoT algorithms, little attention has been given to reducing complexity. To address this gap, this paper proposes an IOT edge computing layer modification based cyber-attack detection edge-cloud architecture that enables quick response by detecting attacks at the Intelligent Buffalo based Secure Edge-enabled Computing layer near their source, offering versatility while decreasing the Cloud’s workload. Additionally, F-AL, a low-complexity multi-attack detection model for deployment at the edge zone, leveraging high accuracy federated active learning approaches, is introduced. The performance evaluation is conducted using the latest BoT-IoT dataset against other Machine Learning and Deep Learning methods which demonstrate that FAL outperforms SVM, FLAD, DL, DNN in terms of accuracy. Cyber-attack detection edge computing Internet of Things F-AL GCN 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-5281086","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":367519324,"identity":"20b26ec5-04a0-4222-a474-c27905a2d295","order_by":0,"name":"J Vinothini","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA80lEQVRIiWNgGAWjYDACZiBmbJBgYGNmSHzwoQIkwtxAWMtBkBZ2hseGM86ARBgJaGEAawES/IzPpDnbGMCW4lVtcJz34OOPOyzy+JiZE6QZ59VG87cDtfyo2IZby2G+ZIODZySK2ZjZEowLtx3PnXGYsYGx58xtnFokm3nMJA62SSS2MfMkJM/cdiy3AaiFmbGNKC38Hw7zzjmWO5+QFn5muBaGxGbehprcDURoMTY4C9GSzDjj2IHcjUAtB/H5hY3/jOGDyra6xPn9B9J/fKipy513/vDBBz8qcGtBB4fB5AGi1QNBHSmKR8EoGAWjYIQAAMoBWRTXrkOgAAAAAElFTkSuQmCC","orcid":"","institution":"Anna University, Chennai","correspondingAuthor":true,"prefix":"","firstName":"J","middleName":"","lastName":"Vinothini","suffix":""},{"id":367519325,"identity":"1db94c0c-6346-4709-adef-023d6a4a8a2f","order_by":1,"name":"Srie Vidhya Janani E","email":"","orcid":"","institution":"Anna University, Chennai","correspondingAuthor":false,"prefix":"","firstName":"Srie","middleName":"Vidhya Janani","lastName":"E","suffix":""}],"badges":[],"createdAt":"2024-10-17 08:38:20","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5281086/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5281086/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":76946555,"identity":"d6dc8acc-f953-45fb-84e3-d87825808547","added_by":"auto","created_at":"2025-02-23 04:31:31","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":457728,"visible":true,"origin":"","legend":"","description":"","filename":"Manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5281086/v1_covered_f1f2e83a-1607-4e51-864c-c647fb325e4a.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"IOT edge computing layer modification based cyber-attack detection using Federated-Active Learning","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"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":"Cyber-attack detection, edge computing, Internet of Things, F-AL, GCN","lastPublishedDoi":"10.21203/rs.3.rs-5281086/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5281086/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe Internet of Things and its practical uses are becoming more widespread as the number of connected devices increases, but it always carries a risk to network security. 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