An Improved Density Peak Clustering with Flexible Manifold Distance and Natural Nearest Neighbors for Network Intrusion Detection

An Improved Density Peak Clustering with Flexible Manifold Distance and Natural Nearest Neighbors for Network Intrusion Detection | 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 Article An Improved Density Peak Clustering with Flexible Manifold Distance and Natural Nearest Neighbors for Network Intrusion Detection Hongbo Wang, Jinyu Zhang, Yu Shen This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4737443/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 12 Mar, 2025 Read the published version in Scientific Reports → Version 1 posted 19 You are reading this latest preprint version Abstract Recently, the field of density peak clustering (DPC) has garnered attention for its ability to intuitively determine the number of classes, identify arbitrarily shaped classes, and automatically detect and exclude anomalies. However, DPC faces challenges in considering only global distribution, resulting in difficulties with group density, and its point allocation strategy may lead to a domino phenomenon. In order to give DPC a broader scope to showcase its talents, this paper suggests a Density Peak Clustering algorithm based on Manifold Distance and Natural Nearest Neighbors (abbreviated as DPC-MDNN), which constructs a nearest neighbor relationship based on manifold distance and introduces representative points using local density to segment the distribution. It adopts an assignment strategy based on representatives and candidates, reducing the domino effect through micro cluster merging. Extensive comparisons with five competing methods on both artificial and real datasets demonstrate that DPC-MDNN can identify clustering centers more accurately and achieve better clustering results. Furthermore, application experiments on two sub-datasets have confirmed that DPC-MDNN can improve the accuracy of network intrusion detection and has high practicality. Physical sciences/Mathematics and computing/Computational science Physical sciences/Mathematics and computing/Scientific data Density peaks clustering Natural nearest neighbors Manifold distance Network intrusion detection Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 12 Mar, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 26 Nov, 2024 Reviewers agreed at journal 24 Nov, 2024 Reviews received at journal 24 Nov, 2024 Reviewers agreed at journal 23 Nov, 2024 Reviewers agreed at journal 23 Nov, 2024 Reviews received at journal 23 Nov, 2024 Reviewers agreed at journal 22 Nov, 2024 Reviewers agreed at journal 22 Nov, 2024 Reviewers agreed at journal 22 Nov, 2024 Reviewers agreed at journal 22 Nov, 2024 Reviewers agreed at journal 22 Nov, 2024 Reviews received at journal 28 Jul, 2024 Reviewers agreed at journal 26 Jul, 2024 Reviewers agreed at journal 19 Jul, 2024 Reviewers invited by journal 19 Jul, 2024 Editor assigned by journal 19 Jul, 2024 Editor invited by journal 16 Jul, 2024 Submission checks completed at journal 15 Jul, 2024 First submitted to journal 14 Jul, 2024 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. 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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-4737443","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":336433270,"identity":"3771c0ac-7630-45c6-b9bc-a49c9c9615e0","order_by":0,"name":"Hongbo Wang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5klEQVRIiWNgGAWjYBAC9gYow0CC+QCQAuEE/Fp4DsC1sAGVJpCmhceASC3svQ8f81TcS9wu3fNN8uePOwz87DkGDD934NHCc9zYmOdMceLOOWe3SfMkPGOQ7HljwNh7BrcWe4k0NsmZbQm5G27kbpNmSDjMYHAjx4CZsQ2PLRJp7D9n/gNpyXkm+QOoxZ4ILWwMHxvAWtgkeEC2SBDSwnOMWeLDsYT6DXeOGVvzpD3jkTjzrOBgLz4t7G2MHxJqEowNbjc/vPnD5o4cf3vyxgc/8WjBNANEHCBBwygYBaNgFIwCLAAAKOVTK5oc9iMAAAAASUVORK5CYII=","orcid":"","institution":"University of Science and Technology Beijing","correspondingAuthor":true,"prefix":"","firstName":"Hongbo","middleName":"","lastName":"Wang","suffix":""},{"id":336433275,"identity":"ddb81e90-90a5-46f0-9068-2e9a19585d95","order_by":1,"name":"Jinyu Zhang","email":"","orcid":"","institution":"University of Science and Technology Beijing","correspondingAuthor":false,"prefix":"","firstName":"Jinyu","middleName":"","lastName":"Zhang","suffix":""},{"id":336433280,"identity":"f30e345a-4c7e-4f6c-9a02-0715c61f2d34","order_by":2,"name":"Yu Shen","email":"","orcid":"","institution":"University of Science and Technology Beijing","correspondingAuthor":false,"prefix":"","firstName":"Yu","middleName":"","lastName":"Shen","suffix":""}],"badges":[],"createdAt":"2024-07-14 07:27:33","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4737443/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4737443/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-025-92509-4","type":"published","date":"2025-03-12T15:57:12+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":78688917,"identity":"eb6c05f3-4b63-42bd-a00c-3f0ceea82b94","added_by":"auto","created_at":"2025-03-17 16:07:15","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":9504877,"visible":true,"origin":"","legend":"","description":"","filename":"202407151.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4737443/v1_covered_024406f0-0b2b-43f2-8fd2-1e0aa8aa8011.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"An Improved Density Peak Clustering with Flexible Manifold Distance and Natural Nearest Neighbors for Network Intrusion Detection","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Density peaks clustering, Natural nearest neighbors, Manifold distance, Network intrusion detection","lastPublishedDoi":"10.21203/rs.3.rs-4737443/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4737443/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Recently, the field of density peak clustering (DPC) has garnered attention for its ability to intuitively determine the number of classes, identify arbitrarily shaped classes, and automatically detect and exclude anomalies. 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