Leveraging Tabular Transformers for AdvancedDetection of Data Exfiltration in DNS Traffic

Leveraging Tabular Transformers for AdvancedDetection of Data Exfiltration in DNS Traffic | 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 Leveraging Tabular Transformers for AdvancedDetection of Data Exfiltration in DNS Traffic Ravi Veerabhadrappa, Poornima Athikatte Sampigerayappa This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6468568/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 Recent advancements in DNS protocols like DNS over HTTPS (DoH), DNS over TLS (DoT), and DNS over QUIC (DoQ) have enabled secure communications for enterprise networks through encrypted connections. While DoH supports secure communication on multiple platforms, malicious implementations can pose significant security risks, including evasion of monitoring, malware communication, and data exfiltration. This study aims to address the security challenges posed by malicious implementations of DNS over HTTPS (DoH) by developing a robust classification model that can differentiate between benign and malicious DoH traffic. We propose a novel model based on the TabTransformer architecture, utilizing self-attention mechanisms. This model transforms network capture features into latent representations, allowing for the effective categorization of DoH traffic. The model is specifically designed to enhance the detection of DNS data exfiltration attacks, particularly those arising from misconfigurations in DNS servers. The performance of the proposed TabTransformer-based attention model is evaluated using the BCCC-CIC-Bell-DNS-2024 dataset. Results demonstrate a significant improvement in the accuracy of classifying DoH traffic as malicious or benign, highlighting the efficacy of embedding generation and attention techniques in enhancing detection capabilities. Our findings show that using the TabTransformer model can significantly improve the monitoring and classification of malicious DoH traffic, reducing security threats in enterprise networks. DNS over HTTPS Data exfiltration Tab Transformer Attention model BCCC-CIC-Bell-DNS-2024 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. 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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-6468568","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":444193034,"identity":"a67d9b3f-20be-4118-a880-e5753289feed","order_by":0,"name":"Ravi Veerabhadrappa","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAqElEQVRIiWNgGAWjYJCCAx8YJCCsBGK1HJzBICFBmhZmHgaYNcQA8/Yzhodt/ljUMbAffsDwcAcRWmTO5Bgczm0DOownzYAh8QwRWiQY0hIO5zaA/JLDwJDYRowW/mcJhy3+ALXwvyFWi0TygcMMbEAtEkTbIvH4wMHeNgnJNolnBgeIdFhi84cff+r4+fmTHz78SYwWOGAD4gOkaBgFo2AUjIJRgAcAAPoXLpaAt4+MAAAAAElFTkSuQmCC","orcid":"","institution":"Siddaganga Institute of Technology","correspondingAuthor":true,"prefix":"","firstName":"Ravi","middleName":"","lastName":"Veerabhadrappa","suffix":""},{"id":444193035,"identity":"e9783dc4-d12d-4685-92f5-c922fe0e7afe","order_by":1,"name":"Poornima Athikatte Sampigerayappa","email":"","orcid":"","institution":"Siddaganga Institute of Technology","correspondingAuthor":false,"prefix":"","firstName":"Poornima","middleName":"Athikatte","lastName":"Sampigerayappa","suffix":""}],"badges":[],"createdAt":"2025-04-17 06:23:46","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-6468568/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6468568/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":80866615,"identity":"9489cf8d-9cb7-42a6-add1-94ce83f4bd7b","added_by":"auto","created_at":"2025-04-18 03:44:03","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1463330,"visible":true,"origin":"","legend":"","description":"","filename":"dcarticle.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6468568/v1_covered_af1e352c-365c-4caf-9b41-244bbc7ed300.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Leveraging Tabular Transformers for AdvancedDetection of Data Exfiltration in DNS Traffic","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"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":"DNS over HTTPS, Data exfiltration, Tab Transformer, Attention model, BCCC-CIC-Bell-DNS-2024","lastPublishedDoi":"10.21203/rs.3.rs-6468568/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6468568/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Recent advancements in DNS protocols like DNS over HTTPS (DoH), DNS over TLS (DoT), and DNS over QUIC (DoQ) have enabled secure communications for enterprise networks through encrypted connections. 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