Word-embedding Approach for Unknown Attributes in Access Control Model

Word-embedding Approach for Unknown Attributes in Access Control Model | 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 Word-embedding Approach for Unknown Attributes in Access Control Model Thanh Duc Bui*, Brajendra Panda This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6584597/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 21 Oct, 2025 Read the published version in Discover Artificial Intelligence → Version 1 posted 10 You are reading this latest preprint version Abstract With the rapid advancements in computing and information technologies, access control models have become increasingly essential as the first line of defense. However, many traditional methods require significant human intervention. While these rule-based approaches, crafted by experienced system engineers, are highly reliable, they are also time-consuming and dependent on human resources that may not always be available. As an alternative, the attribute-based access control (ABAC) model provides greater flexibility in addressing the authorization needs of complex and dynamic systems. Nevertheless, many existing approaches fail to capture the contextual meaning of attribute values, as they are typically treated as categorical data. This paper introduces a framework that leverages advanced Natural Language Processing (NLP) techniques to generate embedding vectors for attribute requests, utilizing a Skip-gram architecture to encode contextual relationships. By extracting well-defined features from these requests, a machine learning classifier is trained to determine authorization decisions accurately. We also explore a variation of the embedding method to accommodate newly introduced values within the system. Our experiments, conducted on real-world datasets, demonstrate the effectiveness of our approach by comparing it against State-of-the-Art (SOTA) models and evaluating its performance in evolving system scenarios. Finally, we discuss our method's advantages and challenges and suggest future research directions. Access Control NLP Unknown Attributes Machine Learning Cybersecurity Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 21 Oct, 2025 Read the published version in Discover Artificial Intelligence → Version 1 posted Editorial decision: Revision requested 09 Jun, 2025 Reviews received at journal 03 Jun, 2025 Reviews received at journal 28 May, 2025 Reviewers agreed at journal 27 May, 2025 Reviewers agreed at journal 26 May, 2025 Reviewers invited by journal 16 May, 2025 Editor invited by journal 14 May, 2025 Editor assigned by journal 08 May, 2025 Submission checks completed at journal 08 May, 2025 First submitted to journal 03 May, 2025 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. 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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-6584597","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":458944297,"identity":"f777dce8-1838-4cfd-bc57-95eb58549118","order_by":0,"name":"Thanh Duc Bui*","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAm0lEQVRIiWNgGAWjYDACCQaGAx8gNBgTBjxAZQdnkKyFmYckLfbSPYaHbdvs8iUbmA/e5iHKFpkzBodz25ItZzOwJVsTp0UiB6SF2UCOgcdMmngtlm31QC3830jQwth22ECagYeNSC13jhUc7Dl33ECymc3Ycg4xWthnN2/+8KOs2kDiePPDG2+I0YIAzKQpHwWjYBSMglGADwAAcGQo2R4zgV0AAAAASUVORK5CYII=","orcid":"","institution":"University of Arkansas at Fayetteville","correspondingAuthor":true,"prefix":"","firstName":"Thanh","middleName":"Duc","lastName":"Bui*","suffix":""},{"id":458944298,"identity":"31960b5f-ab2d-4f88-b8d1-36f5dcfdb6eb","order_by":1,"name":"Brajendra Panda","email":"","orcid":"","institution":"University of Arkansas at Fayetteville","correspondingAuthor":false,"prefix":"","firstName":"Brajendra","middleName":"","lastName":"Panda","suffix":""}],"badges":[],"createdAt":"2025-05-03 14:23:21","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6584597/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6584597/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s44163-025-00551-y","type":"published","date":"2025-10-21T16:16:12+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":94490798,"identity":"bf403d34-68d3-436b-851c-8f330701e158","added_by":"auto","created_at":"2025-10-27 17:15:15","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1345309,"visible":true,"origin":"","legend":"","description":"","filename":"ThanhBuiWordembeddingApproachforUnknownAttributesinAccessControlModel.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6584597/v1_covered_a12ad2d7-3c37-4ac5-9d7d-1ed7b21a5c02.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Word-embedding Approach for Unknown Attributes in Access Control Model","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":"discover-artificial-intelligence","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"diai","sideBox":"Learn more about [Discover Artificial Intelligence](https://www.springer.com/44163)","snPcode":"","submissionUrl":"","title":"Discover Artificial Intelligence","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Access Control, NLP, Unknown Attributes, Machine Learning, Cybersecurity","lastPublishedDoi":"10.21203/rs.3.rs-6584597/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6584597/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eWith the rapid advancements in computing and information technologies, access control models have become increasingly essential as the first line of defense. 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