Deep Learning-Based Intrusion Detection System for Evolving Cyber Threats in High-Speed Networks

Deep Learning-Based Intrusion Detection System for Evolving Cyber Threats in High-Speed Networks | 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 Deep Learning-Based Intrusion Detection System for Evolving Cyber Threats in High-Speed Networks T S S SRIHARI KRISHNA GOPAL BODA, KAVYA LATHA HASRHINI DONTULA This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8773152/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 rapid growth of computer networks, cloud computing, and Internet-based services has substantially increased the attack surface against cyber threats. The latest cyber attacks, such as zero-day attacks, advanced persistent threats (APTs), and polymorphic malware, are constantly evolving and are difficult to identify using traditional security solutions. Traditional intrusion detection systems (IDS), which are based on static rules and known attack signatures, are unable to cope with these evolving threats and tend to produce high false-positive rates, thereby reducing their efficiency in practical scenarios. This paper presents an intelligent deep learning-based intrusion detection system that can automatically identify complex and non-linear patterns from network traffic data. Using deep neural networks for feature extraction and classification, the proposed system improves the accuracy, scalability, and real-time performance of intrusion detection. Experimental results show that the proposed system outperforms traditional IDS techniques and can be effectively used in modern high-speed networks. Intrusion Detection System (IDS) Deep Learning Cybersecurity Network Traffic Analysis Zero-Day Attacks Advanced Persistent Threats Anomaly Detection Neural Networks Real-Time Network Security High-Speed Networks 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-8773152","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":589632806,"identity":"e3bb3e6b-a3f8-4a59-b3e2-b08e85baf2ea","order_by":0,"name":"T S S SRIHARI KRISHNA GOPAL BODA","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA80lEQVRIiWNgGAWjYBACCQbGBmYgnQBEjA8+AFls7CRoYTacAdLCTFALAwNMC5s0DwOUiw9Izj7c/Llwh10eP3vyM2mbX9vk+ZgZGD98zMGtRZovsU165pnkYsmeZ8bWuX23DduYGZglZ27DrUWOh7GNmbeNOXHDjQTD27k9t4FcoHd48Wtp/szbVg/Ukv5B2rLntj1BLdI8jA3SvG2HgVpyjKQZftxOJKhFsoexDajlONAvb4oNextuJ7cxMzbj9YvEGfbHQIdVA0MsfeODH39u285vbz744SMeLaiAsQ1MNhCrHgT+kKJ4FIyCUTAKRgoAAATLTius1jrPAAAAAElFTkSuQmCC","orcid":"","institution":"pragati engineering college","correspondingAuthor":true,"prefix":"","firstName":"T","middleName":"S S SRIHARI KRISHNA GOPAL","lastName":"BODA","suffix":""},{"id":589632808,"identity":"79af8c03-87b9-4b5f-a957-c288d6223c9e","order_by":1,"name":"KAVYA LATHA HASRHINI DONTULA","email":"","orcid":"","institution":"pragati engineering college","correspondingAuthor":false,"prefix":"","firstName":"KAVYA","middleName":"LATHA HASRHINI","lastName":"DONTULA","suffix":""}],"badges":[],"createdAt":"2026-02-03 08:39:18","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-8773152/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8773152/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":102570128,"identity":"25d0adee-ea9a-4334-b39a-3bbf4bb7bdb3","added_by":"auto","created_at":"2026-02-13 06:56:04","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":648763,"visible":true,"origin":"","legend":"","description":"","filename":"deeplearningpaper.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8773152/v1_covered_7cca6919-982e-4307-8292-ea165f566da9.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Deep Learning-Based Intrusion Detection System for Evolving Cyber Threats in High-Speed Networks","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"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":"Intrusion Detection System (IDS), Deep Learning, Cybersecurity, Network Traffic Analysis, Zero-Day Attacks, Advanced Persistent Threats, Anomaly Detection, Neural Networks, Real-Time Network Security, High-Speed Networks","lastPublishedDoi":"10.21203/rs.3.rs-8773152/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8773152/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"The rapid growth of computer networks, cloud computing, and Internet-based services has substantially increased the attack surface against cyber threats. The latest cyber attacks, such as zero-day attacks, advanced persistent threats (APTs), and polymorphic malware, are constantly evolving and are difficult to identify using traditional security solutions. Traditional intrusion detection systems (IDS), which are based on static rules and known attack signatures, are unable to cope with these evolving threats and tend to produce high false-positive rates, thereby reducing their efficiency in practical scenarios. This paper presents an intelligent deep learning-based intrusion detection system that can automatically identify complex and non-linear patterns from network traffic data. Using deep neural networks for feature extraction and classification, the proposed system improves the accuracy, scalability, and real-time performance of intrusion detection. Experimental results show that the proposed system outperforms traditional IDS techniques and can be effectively used in modern high-speed networks.","manuscriptTitle":"Deep Learning-Based Intrusion Detection System for Evolving Cyber Threats in High-Speed Networks","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-12 12:29:40","doi":"10.21203/rs.3.rs-8773152/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","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}}],"origin":"","ownerIdentity":"23458718-3e1d-4ca6-973a-9987e0eb5fa3","owner":[],"postedDate":"February 12th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-02-13T06:55:51+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-12 12:29:40","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8773152","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8773152","identity":"rs-8773152","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}