Representing Network Frontiers based on Spatio-temporal Graph Transformations

Representing Network Frontiers based on Spatio-temporal Graph Transformations | 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 Representing Network Frontiers based on Spatio-temporal Graph Transformations Hafiz Muhammad Jamsheed Nazir, Weihong Han, Shudong Li This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4108019/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 Purpose-Spatio-temporal prediction in cyber elements can be enhanced by incorporating geographic features into the estimation process for cyber elements. It highlights a deficiency in existing studies that do not incorporate geographic characteristics when estimating network security factors. Methodology-This methodology is then assessed through benchmark experiments using ARIMA and LSTM models, with the spatiotemporal graph convolution prediction model demonstrating superior forecasting performance based on evaluation criteria such as Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE). Findings-This leads to the creation of a spatiotemporal network vulnerability dataset, demonstrating the feasibility of integrating spatial and temporal dimensions into network vulnerability analysis. Research Limitations -The identified gap in studies integrating geographic characteristics in estimating network security factors suggests a need for further research in this area. Future studies could explore different approaches, datasets, and applications to build upon the current findings and contribute to the evolving field of spatio-temporal prediction in cyberspace. Practical Implications-The practical implications of incorporating geographic features and utilizing the proposed spatiotemporal graph convolution model include improved network security planning, enhanced predictive capabilities, informed data-driven decision-making, benchmarking against traditional models, adaptation to evolving threats, and resource optimization. These implications highlight the potential practical benefits for organizations seeking advanced and effective approaches to cybersecurity. Originality-The originality of incorporating geographic features, addressing a gap in the literature, introducing an innovative methodology, creating a unique spatiotemporal dataset, and proposing a sophisticated spatiotemporal graph convolution model with demonstrated superior forecasting performance. These contributions collectively advance the understanding and application of spatio-temporal prediction in the field of cyberspace security. Cyberspace data Geographical Spatiotemporal data Skewers of a spatiotemporal graph Forecasting model 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-4108019","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":280241176,"identity":"93e06b97-76b4-445e-b61e-a919a31a177f","order_by":0,"name":"Hafiz Muhammad Jamsheed Nazir","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA1klEQVRIiWNgGAWjYFACxgYwxcbAfADGJlYLG1sCsVpggI3HgDgt8u2HGx8X1Ngw8Mn3fJP4ucNGjoH98NEN+LQYnElsNp5xLA3oMN5tkr1n0owZeNLSbuDVIsHYJs3bcBisRYK37XBigwSPGV4t8jMY23/zNvwHauF5JvmXGC0MNxjbmHkbDoC0sEkTZQvIL9I8x5J52NjSjK1l29KM2Qj5Rb79+MPPPDV2cvLNhx/efNtmI8fPfvgYfodBAQ8Qs0iAWGzEKIcB5g+kqB4Fo2AUjIKRAwD+f0AyaLB4DgAAAABJRU5ErkJggg==","orcid":"","institution":"Guangzhou University","correspondingAuthor":true,"prefix":"","firstName":"Hafiz","middleName":"Muhammad Jamsheed","lastName":"Nazir","suffix":""},{"id":280241178,"identity":"ee740144-59a6-4084-823f-71416bce720b","order_by":1,"name":"Weihong Han","email":"","orcid":"","institution":"Guangzhou University","correspondingAuthor":false,"prefix":"","firstName":"Weihong","middleName":"","lastName":"Han","suffix":""},{"id":280241180,"identity":"f52cddb4-9800-4ab6-ae2d-426878a18491","order_by":2,"name":"Shudong Li","email":"","orcid":"","institution":"Guangzhou University","correspondingAuthor":false,"prefix":"","firstName":"Shudong","middleName":"","lastName":"Li","suffix":""}],"badges":[],"createdAt":"2024-03-15 12:56:52","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4108019/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4108019/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":53611124,"identity":"8de27f29-fc67-4a2d-85b3-c79f46e85c7e","added_by":"auto","created_at":"2024-03-28 05:15:35","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":610359,"visible":true,"origin":"","legend":"","description":"","filename":"1.Representingnetworkfrontiers.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4108019/v1_covered_50190aff-421b-41ba-ac2e-9f3b71a2f58d.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Representing Network Frontiers based on Spatio-temporal Graph Transformations","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":"Cyberspace data, Geographical, Spatiotemporal data, Skewers of a spatiotemporal graph, Forecasting model","lastPublishedDoi":"10.21203/rs.3.rs-4108019/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4108019/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003ePurpose-Spatio-temporal prediction in cyber elements can be enhanced by incorporating geographic features into the estimation process for cyber elements. 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Future studies could explore different approaches, datasets, and applications to build upon the current findings and contribute to the evolving field of spatio-temporal prediction in cyberspace.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePractical Implications-The practical implications of incorporating geographic features and utilizing the proposed spatiotemporal graph convolution model include improved network security planning, enhanced predictive capabilities, informed data-driven decision-making, benchmarking against traditional models, adaptation to evolving threats, and resource optimization. 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