DeepSecure Net: An Ensemble DPATMFNet approach with Enhanced Feature Selection for Advanced SDN Network Intrusion Detection System

DeepSecure Net: An Ensemble DPATMFNet approach with Enhanced Feature Selection for Advanced SDN Network Intrusion Detection System | 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 DeepSecure Net: An Ensemble DPATMFNet approach with Enhanced Feature Selection for Advanced SDN Network Intrusion Detection System Jalaiah Saikam, Koteswararao Ch This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4817164/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 By enabling the control and administration of the entire network from a single location, a Software-Defined Network (SDN) was created to streamline network administration. SDN controllers find intruders appealing because they make good targets. Attackers can take control of an SDN controller and use it to route traffic according to their requirements, which can have disastrous effects on the network. Although integrating SDN with deep learning strategies opens up novel avenues for IDS deployment defense, the detection models' efficacy depends on the quality of the training data. While deep learning for non-identifiable detection systems (NIDSs) has yielded promising results recently for several problems, most studies overlooked the impact of imbalanced and redundant datasets. Therefore, to improve the detection of network intrusions via binary and multiclass categorization, we proposed a novel enhanced ensemble DL-based Dual Parallel Attention Transformer (DPAT) with a Modular Deep Fully Convolutional Network (MDFCN), termed DPATMFNet approach. An Enhanced AlexNet method extracts the features from the input data. The Boosted Binary Meerkat Optimization Algorithm (BBMOA) is applied to choose the key features. The proposed system categorizes attacks, separates malicious from benign traffic, and identifies outstanding performance sub-attack types. Three of the most current realistic datasets were used for training and evaluation to demonstrate the effectiveness of the suggested system. We examined and contrasted its performance with that of other IDSs. The experimental findings indicate that the proposed system performs better than others at identifying various attacks. The suggested datasets achieve accuracy, detection rate, and precision above 99% compared to existing approaches. The results show how effective the proposed model is at obtaining high accuracy while requiring a shorter training period. Software-Defined Network (SDN) Dual Parallel Attention Transformer (DPAT) Modular Deep Fully Convolutional Network (MDFCN) Boosted Binary Meerkat Optimization Algorithm (BBMOA) 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-4817164","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":341432879,"identity":"6f854ac7-1d62-4431-a1b0-7ec4e0b9a8a0","order_by":0,"name":"Jalaiah Saikam","email":"","orcid":"","institution":"VIT-AP University","correspondingAuthor":false,"prefix":"","firstName":"Jalaiah","middleName":"","lastName":"Saikam","suffix":""},{"id":341432880,"identity":"0e81234d-cace-432a-812d-aa3c4bd179e9","order_by":1,"name":"Koteswararao Ch","email":"data:image/png;base64,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","orcid":"","institution":"VIT-AP University","correspondingAuthor":true,"prefix":"","firstName":"Koteswararao","middleName":"","lastName":"Ch","suffix":""}],"badges":[],"createdAt":"2024-07-28 14:44:57","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4817164/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4817164/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":65993229,"identity":"f9d569b0-2ab2-42f0-998d-9c110e2da998","added_by":"auto","created_at":"2024-10-05 20:31:28","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1858729,"visible":true,"origin":"","legend":"","description":"","filename":"ClusterComputingManuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4817164/v1_covered_e432059c-2a6d-4fb5-be35-fc82f671d6eb.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"DeepSecure Net: An Ensemble DPATMFNet approach with Enhanced Feature Selection for Advanced SDN Network Intrusion Detection System","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":"Software-Defined Network (SDN), Dual Parallel Attention Transformer (DPAT), Modular Deep Fully Convolutional Network (MDFCN), Boosted Binary Meerkat Optimization Algorithm (BBMOA)","lastPublishedDoi":"10.21203/rs.3.rs-4817164/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4817164/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eBy enabling the control and administration of the entire network from a single location, a Software-Defined Network (SDN) was created to streamline network administration. 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