Intrusion detection model based on multi-kernel approximation and multi-layer neural network

Intrusion detection model based on multi-kernel approximation and multi-layer neural network | 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 Intrusion detection model based on multi-kernel approximation and multi-layer neural network Yukun Wu, Hui Li, Yunzhi Chen, Wei William Lee This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3887865/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 Aiming to address the problems of low time efficiency and poor generalization ability in support vector machine(SVM) model when dealing with large-scale network intrusions, this paper suggests a large-scale robust intrusion detection model that combines multi-layer neural network and multi-kernel approximate support vector machines. The multi-layer neural network carries out representation learning, learns the essential properties of the dataset, and performs feature reduction on the dataset. The multi-kernel approximation SVM using random Fourier characteristics to perform kernel approximation can handle large-scale datasets. The model employs the gradient descent method to train neural networks and multi-kernel SVM from start to finish. Our model was tested on three intrusion detection datasets of varying scales: NSL-KDD, UNSW-NB15, and CICIDS2017; and compared with conventional machine learning models, deep learning models, and SVM models of different variants. The experimental findings show that our model has higher classification performance and better robustness when processing large-scale datasets, and has more advantages in terms of time complexity. Intrusion detection Kernel approximation Support vector machine Multi-layer neural network Random Fourier features Full Text 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-3887865","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":288471758,"identity":"6c20145a-8098-4202-8732-8d4510f04ba0","order_by":0,"name":"Yukun Wu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA/UlEQVRIiWNgGAWjYBACNvmDDQc+VNjI8TMwMDN8MLCxI6iFX4K58eGMM2nGkg0MzIwzCtKSCWqRnMHebMzbdjhxA1ALM8+HQ4wNhLQY3G5sk+ZhY07cIJFjbGxjcICZgf3w0Q14tdw52CY5h4fNeDtQS3KOwR0+Bp60tBt4tRxIbJN4I8Eju3N2jvHhHINnzAwSPGaEtfAYSDBuuA3UYmFwmLGBkBbJGYnNhjwJBoogLckMxGjh5zkIDOQDCcaS858VG/YYpCWzEfILG3v7gwMf//2X4+c5vFnixx8bO372w8fwakECHAYQQ4hUDgLsD0hQPApGwSgYBSMJAACsy1EMO41fsgAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0003-1846-8616","institution":"Hangzhou Vocational and Technical College","correspondingAuthor":true,"prefix":"","firstName":"Yukun","middleName":"","lastName":"Wu","suffix":""},{"id":288471759,"identity":"35bd3371-df23-4452-a1f0-a88cecdf42f8","order_by":1,"name":"Hui Li","email":"","orcid":"","institution":"Zhejiang Anteng Information Technology Co.,Ltd","correspondingAuthor":false,"prefix":"","firstName":"Hui","middleName":"","lastName":"Li","suffix":""},{"id":288471760,"identity":"d4686ce0-fe07-4072-ba96-52b829f7e330","order_by":2,"name":"Yunzhi Chen","email":"","orcid":"","institution":"Hangzhou Vocational and Technical College","correspondingAuthor":false,"prefix":"","firstName":"Yunzhi","middleName":"","lastName":"Chen","suffix":""},{"id":288471761,"identity":"63ffd8c9-b584-4fdb-a7d7-64bb56ade4bc","order_by":3,"name":"Wei William Lee","email":"","orcid":"","institution":"Zhejiang University of Technology Chaohui Campus: Zhejiang University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Wei","middleName":"William","lastName":"Lee","suffix":""}],"badges":[],"createdAt":"2024-01-22 12:07:37","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3887865/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3887865/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":57065142,"identity":"1f69b2d7-72f3-46d5-ab14-359ed6daeac8","added_by":"auto","created_at":"2024-05-24 07:02:05","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":373435,"visible":true,"origin":"","legend":"","description":"","filename":"Manuscript20240126.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3887865/v1_covered_d6ac3269-ce9e-4796-b6c4-2e659e6b5914.pdf"}],"financialInterests":"","formattedTitle":"Intrusion detection model based on multi-kernel approximation and multi-layer neural network","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":"Intrusion detection, Kernel approximation, Support vector machine, Multi-layer neural network, Random Fourier features","lastPublishedDoi":"10.21203/rs.3.rs-3887865/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3887865/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAiming to address the problems of low time efficiency and poor generalization ability in support vector machine(SVM) model when dealing with large-scale network intrusions, this paper suggests a large-scale robust intrusion detection model that combines multi-layer neural network and multi-kernel approximate support vector machines. 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