Enhancing Ddos Attack Detection Using Machine Learning

Enhancing Ddos Attack Detection Using Machine 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 Enhancing Ddos Attack Detection Using Machine Learning Shanmugam M, Govindharaj I, Nidhish Kumar P This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7422019/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 Denial of Service (DoS) attacks aim to disrupt the availability of a machine or network resource, preventing legitimate users from accessing services. A more advanced variant, Distributed Denial of Service (DDoS), involves multiple compromised systems attacking a target, often overwhelming it with malicious traffic. DDoS attacks are executed for various purposes, including financial extortion, political motives, or simply for disruption. This paper presents an advanced DDoS detection system designed for Software-Defined Networking (SDN) environments, utilizing Deep Learning techniques. Specifically, our model leverages the Gated Recurrent Unit (GRU) algorithm to analyze network traffic patterns and effectively detect DDoS attacks. By utilizing the CICDDoS2019 dataset, our proposed approach demonstrates superior accuracy compared to traditional methods, reinforcing the security of SDN networks against evolving cyber threats. Gated Recurrent Unit (GRU) Distributed Denial of Service (DDoS) Deep Learning Software-Defined Networking (SDN) 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-7422019","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":513518055,"identity":"282231a3-92f8-4382-9578-e74af3829242","order_by":0,"name":"Shanmugam M","email":"","orcid":"","institution":"Sri Manakula Vinayagar Engineering College","correspondingAuthor":false,"prefix":"","firstName":"Shanmugam","middleName":"","lastName":"M","suffix":""},{"id":513518056,"identity":"381db69b-5d0f-4caa-9bc6-8ce67365af03","order_by":1,"name":"Govindharaj I","email":"","orcid":"","institution":"Vel Tech Rangarajan Dr. Sagunthala R\u0026D Institute of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Govindharaj","middleName":"","lastName":"I","suffix":""},{"id":513518065,"identity":"3936b048-72f9-437b-b775-61a27e3b09ec","order_by":2,"name":"Nidhish Kumar P","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABFElEQVRIie3PsUrEMBjA8ZRAXKK3VirXJxAihZQDaV8lJZDJwbFDqT0O4lKc+ySdewR6S/DWgxu8vkHhoDiJORBEaJXbHPKHQCDfD74AYLP9w4g58HSZXSyfe5befz+hKYK+yHWpWnDQ4gxCdkI4nVR/Lxb6q/XxMcvnoHmgB6a38W3F1z3IInDlFaNkIRH3qlYFTqFDwtJ9Uu8Ed0HLAbppxhdrMYWXRZOsnJK4TO8Z1ZqajzQAuWyCzAZD8icJMXET+RpTvR0A+PiNYGQIZBghYUjj1JsSAUdOk4UUgYdbdVdhqAjTPKk3MnCTF46nSAhVd8RZ7sdv3bJ7T6OYKtj1/RDN/WqcTGSG8TnzNpvNZvvZJ1aUXNXO4jrjAAAAAElFTkSuQmCC","orcid":"","institution":"Sri Manakula Vinayagar Engineering College","correspondingAuthor":true,"prefix":"","firstName":"Nidhish","middleName":"Kumar","lastName":"P","suffix":""}],"badges":[],"createdAt":"2025-08-21 04:08:18","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7422019/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7422019/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":104782251,"identity":"d7f6f3e5-cbdb-4bb8-9125-f685e096b580","added_by":"auto","created_at":"2026-03-17 07:57:03","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":569894,"visible":true,"origin":"","legend":"","description":"","filename":"EnhancedDDosAttackDetectionusingML.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7422019/v1_covered_b2de3f20-b1b5-4367-9dfa-d7506084ea54.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eEnhancing Ddos Attack Detection Using Machine Learning\u003c/p\u003e","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":"Gated Recurrent Unit (GRU), Distributed Denial of Service (DDoS), Deep Learning, Software-Defined Networking (SDN)","lastPublishedDoi":"10.21203/rs.3.rs-7422019/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7422019/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eDenial of Service (DoS) attacks aim to disrupt the availability of a machine or network resource, preventing legitimate users from accessing services. A more advanced variant, Distributed Denial of Service (DDoS), involves multiple compromised systems attacking a target, often overwhelming it with malicious traffic. DDoS attacks are executed for various purposes, including financial extortion, political motives, or simply for disruption. This paper presents an advanced DDoS detection system designed for Software-Defined Networking (SDN) environments, utilizing Deep Learning techniques. Specifically, our model leverages the Gated Recurrent Unit (GRU) algorithm to analyze network traffic patterns and effectively detect DDoS attacks. 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