{"paper_id":"1630ebb7-7ea2-4f7f-b126-4c06e0359bb7","body_text":"FRCNC - An Enhancing Model for Classifying Packet Traffic at Internet Routers | 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 FRCNC - An Enhancing Model for Classifying Packet Traffic at Internet Routers Vuong Xuan Chi This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8221589/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 7 You are reading this latest preprint version Abstract In the modern networked world, it is a major challenge to control and categorise the traffic of the network, as per various applications. The networks are still evolving and are now offering various services like video streaming, VoIP, and enterprise data and so to manage the traffic effectively and therefore have optimization bandwidth, minimization of delay and Quality of Service (QoS). Traffic classification allows identifying and assigning network resources to per application type and enhances performance and reduced congestion. The paper suggests that one of the possible solutions is Federated Reinforcement Learning Convolutional Neural Networks Classification model (FRCNC). In model, Convolutional Neural Networks (CNN) extract specific features from network packet data, supporting the identification of traffic patterns. At the same time, Federated Reinforcement Learning (FRL) enhances the classification of network traffic by application. The method enables network routers to train and refresh a common model without sharing data and ensures privacy and does not overload central servers. Also, it adapts dynamically to network conditions on thresholds. FRCNC enhances efficiency of traffic classification and management, maximises QoS and distributes resources based on the application types and minimises network congestion. The model has a great potential in the management of future network systems, where the performance and security requirements are constantly becoming more and more complicated. packet classification traffic network FRCNC model QoS network performance Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 23 Mar, 2026 Reviewers agreed at journal 03 Mar, 2026 Reviewers agreed at journal 02 Mar, 2026 Reviewers invited by journal 02 Mar, 2026 Editor assigned by journal 28 Nov, 2025 Submission checks completed at journal 28 Nov, 2025 First submitted to journal 27 Nov, 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. 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-8221589\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":false,\"archivedVersions\":[],\"articleType\":\"Research Article\",\"associatedPublications\":[],\"authors\":[{\"id\":600242139,\"identity\":\"d640dab3-4efe-45cb-987b-7252d3b0a849\",\"order_by\":0,\"name\":\"Vuong Xuan Chi\",\"email\":\"data:image/png;base64,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\",\"orcid\":\"\",\"institution\":\"Trường ĐH Nguyễn Tất Thành\",\"correspondingAuthor\":true,\"prefix\":\"\",\"firstName\":\"Vuong\",\"middleName\":\"Xuan\",\"lastName\":\"Chi\",\"suffix\":\"\"}],\"badges\":[],\"createdAt\":\"2025-11-27 11:38:20\",\"currentVersionCode\":1,\"declarations\":\"\",\"doi\":\"10.21203/rs.3.rs-8221589/v1\",\"doiUrl\":\"https://doi.org/10.21203/rs.3.rs-8221589/v1\",\"draftVersion\":[],\"editorialEvents\":[],\"editorialNote\":\"\",\"failedWorkflow\":false,\"files\":[{\"id\":104402030,\"identity\":\"c25197e9-8ab6-490d-86c9-d7ceef46f135\",\"added_by\":\"auto\",\"created_at\":\"2026-03-11 12:14:03\",\"extension\":\"pdf\",\"order_by\":1,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"manuscript-pdf\",\"size\":1088905,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"FinalVuongXuanChiedited.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8221589/v1_covered_da0d5463-d9df-42d4-85a8-1e25ba9aef8c.pdf\"}],\"financialInterests\":\"No competing interests reported.\",\"formattedTitle\":\"FRCNC - An Enhancing Model for Classifying Packet Traffic at Internet Routers\",\"fulltext\":[],\"fulltextSource\":\"\",\"fullText\":\"\",\"funders\":[],\"hasAdminPriorityOnWorkflow\":false,\"hasManuscriptDocX\":false,\"hasOptedInToPreprint\":true,\"hasPassedJournalQc\":\"\",\"hasAnyPriority\":false,\"hideJournal\":false,\"highlight\":\"\",\"institution\":\"\",\"isAcceptedByJournal\":false,\"isAuthorSuppliedPdf\":true,\"isDeskRejected\":\"\",\"isHiddenFromSearch\":false,\"isInQc\":false,\"isInWorkflow\":false,\"isPdf\":true,\"isPdfUpToDate\":true,\"isWithdrawnOrRetracted\":false,\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"telecommunication-systems\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":false,\"externalIdentity\":\"tels\",\"sideBox\":\"Learn more about [Telecommunication Systems](https://www.springer.com/journal/11235)\",\"snPcode\":\"11235\",\"submissionUrl\":\"https://submission.nature.com/new-submission/11235/3\",\"title\":\"Telecommunication Systems\",\"twitterHandle\":\"\",\"acdcEnabled\":true,\"dfaEnabled\":true,\"editorialSystem\":\"em\",\"reportingPortfolio\":\"Springer Hybrid\",\"inReviewEnabled\":true,\"inReviewRevisionsEnabled\":false},\"keywords\":\"packet classification, traffic network, FRCNC model, QoS, network performance\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-8221589/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-8221589/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003cp\\u003eIn the modern networked world, it is a major challenge to control and categorise the traffic of the network, as per various applications. 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