PerFedHypID: A Personalized Federated Hypernetworks based aggregation approach for Intrusion Detection Systems

PerFedHypID: A Personalized Federated Hypernetworks based aggregation approach for Intrusion Detection Systems | 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 Article PerFedHypID: A Personalized Federated Hypernetworks based aggregation approach for Intrusion Detection Systems Chunduru Abhijit, Y Annie Jerusha, Syed Ibrahim S P, Vijay Varadharajan This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4767476/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 30 Sep, 2025 Read the published version in Scientific Reports → Version 1 posted 13 You are reading this latest preprint version Abstract Traditional Network Intrusion Detection Systems (NIDS) face scalability challenges due to the vast amount of data generated by Internet of Things (IoT) devices, coupled with growing privacy concerns. Federated Learning (FL) emerges as a distributed, privacy-preserving learning paradigm that trains deep learning models locally, mitigating privacy risks associated with centralized data processing. However, conventional FL strategies entail limitations. First, they require all clients to use the same model architectures. This makes personalized learning harder, especially when there is non-IID (independent and identically distributed) heterogeneous data. Secondly, the weight aggregation process in FL introduces communication overhead, potentially slowing down aggregation. Also, even though there are encryption methods like homomorphic encryption and differential privacy, encrypted weights take more time to process and can be hacked to see how the data is distributed. This is especially true in dynamic IoT environments where attack types cause data distributions to change all the time. To address these challenges, we propose PerFedHypID: A Personalized Federated Hypernetworks-based aggregation strategy for Intrusion Detection Systems. Our approach incorporates embedding vectors instead of weights, which are lighter and offer enhanced personalization capabilities. PerFedHypID leverages personalized layers and hypernetwork aggregation for efficient and personalized model aggregation. We extensively evaluated our proposed system on the CSE-CICIDS-2018 dataset under various non-IID heterogeneous settings. Our results demonstrate robust performance compared to state-of-the-art personalized federated learning algorithms. Physical sciences/Mathematics and computing/Computational science Physical sciences/Mathematics and computing/Computer science Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 30 Sep, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 08 Jan, 2025 Reviews received at journal 13 Dec, 2024 Reviewers agreed at journal 02 Oct, 2024 Reviewers agreed at journal 30 Sep, 2024 Reviews received at journal 18 Aug, 2024 Reviewers agreed at journal 07 Aug, 2024 Reviewers agreed at journal 28 Jul, 2024 Reviewers agreed at journal 27 Jul, 2024 Reviewers invited by journal 27 Jul, 2024 Editor assigned by journal 27 Jul, 2024 Editor invited by journal 27 Jul, 2024 Submission checks completed at journal 24 Jul, 2024 First submitted to journal 19 Jul, 2024 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-4767476","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":342108620,"identity":"46f9df4a-3af5-4d38-8ce0-ef0b3930871c","order_by":0,"name":"Chunduru Abhijit","email":"","orcid":"","institution":"Vellore Institute of Technology University","correspondingAuthor":false,"prefix":"","firstName":"Chunduru","middleName":"","lastName":"Abhijit","suffix":""},{"id":342108621,"identity":"494d3771-bff0-48da-a380-961d5f53a51c","order_by":1,"name":"Y Annie Jerusha","email":"","orcid":"","institution":"Vellore Institute of Technology University","correspondingAuthor":false,"prefix":"","firstName":"Y","middleName":"Annie","lastName":"Jerusha","suffix":""},{"id":342108622,"identity":"b10d5b62-4bd1-4307-b38d-26a68d4f7b2c","order_by":2,"name":"Syed Ibrahim S P","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA/UlEQVRIiWNgGAWjYJACicQGBgYDZjDbhoENKki0ljQGNjZitDCCtEDYhxlg1uAE8jOSD954uOOevDk7dwLjzx3nE/vkGxg//GCwyMOlxeBGWrJF4pliw53NvBuYec/cTmxjY2CW7GGQKMapRSLHTCKxLYFxw2GgFsa228Ygv0hDPYjDYfnfQFrsQVoYf7adA2lh/o1PC8ONHDaQlkSQFgbetgNyQC1seG0xOPPMGOiXhGSQlsO8Z5KBWhLbLHsM8DisPfnhzZ87Emw3nD+78eHPHXY88s2HD9/4UVGH22ECCQj2AUawMqRowgr4DyBxGHGaPApGwSgYBSMZAAB7m1RQ6wapMwAAAABJRU5ErkJggg==","orcid":"","institution":"Vellore Institute of Technology University","correspondingAuthor":true,"prefix":"","firstName":"Syed","middleName":"Ibrahim S","lastName":"P","suffix":""},{"id":342108623,"identity":"3e0cc085-2a9f-4f43-8e36-e9b245f5ed0a","order_by":3,"name":"Vijay Varadharajan","email":"","orcid":"","institution":"University of Newcastle Australia","correspondingAuthor":false,"prefix":"","firstName":"Vijay","middleName":"","lastName":"Varadharajan","suffix":""}],"badges":[],"createdAt":"2024-07-19 10:18:04","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4767476/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4767476/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-025-11659-7","type":"published","date":"2025-09-30T15:57:21+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":92883732,"identity":"a5efdb2f-9838-44e0-ba78-43ca3e443e87","added_by":"auto","created_at":"2025-10-06 16:08:43","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2066677,"visible":true,"origin":"","legend":"","description":"","filename":"PerFedScientificReports.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4767476/v1_covered_64238b9a-02f4-4cf6-8ec4-a8cc59d9951f.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"PerFedHypID: A Personalized Federated Hypernetworks based aggregation approach for Intrusion Detection Systems","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"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":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-4767476/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4767476/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Traditional Network Intrusion Detection Systems (NIDS) face scalability challenges due to the vast amount of data generated by Internet of Things (IoT) devices, coupled with growing privacy concerns. Federated Learning (FL) emerges as a distributed, privacy-preserving learning paradigm that trains deep learning models locally, mitigating privacy risks associated with centralized data processing. However, conventional FL strategies entail limitations. First, they require all clients to use the same model architectures. This makes personalized learning harder, especially when there is non-IID (independent and identically distributed) heterogeneous data. Secondly, the weight aggregation process in FL introduces communication overhead, potentially slowing down aggregation. Also, even though there are encryption methods like homomorphic encryption and differential privacy, encrypted weights take more time to process and can be hacked to see how the data is distributed. This is especially true in dynamic IoT environments where attack types cause data distributions to change all the time. 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