E2E-EmbedDetector: A Lightweight Entity-Embedding Model for Ethereum Phishing Detection

E2E-EmbedDetector: A Lightweight Entity-Embedding Model for Ethereum Phishing 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 Research Article E2E-EmbedDetector: A Lightweight Entity-Embedding Model for Ethereum Phishing Detection Abhishree Sinha This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9510834/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 Phishing attacks pose a significant security issue in Ethereum-based blockchain systems. Existing solutions, like TEGDetector, address these attacks by analysing how transactions evolve over time using Transaction Evolution Graphs (TEGs) constructed via time slicing, followed by a dynamic graph classifier that captures both spatial structure and temporal evolution with learned time coefficients. However, building and managing these graphs across multiple stages makes the overall approach complex and difficult to implement. In this work, we propose E2E-EmbedDetector, a lightweight end-to-end neural classification model that works directly with raw transaction data. The model learns embedding representations for important entities such as From, To, and ContractAddress, and also used two additional numeric features: transactional value and a derived input length. We train and evaluate the model on a balanced dataset of 50,000 Ethereum transaction using an 80/20 stratified split. The model achieves an accuracy of 95.63%, precision of 0.9265, recall of 0.9912, an F1 score of 0.9578, a ROC-AUC score of 0.9915 and a PR-AUC score of 0.9909. These results show that strong phishing can be achieved using a simpler and more practical tabular approach, without relying on complex temporal graph- based networks. Artificial Intelligence and Machine Learning phishing detection blockchain security Ethereum transaction entity embeddings neural networks Full Text Additional Declarations The authors declare no competing interests. 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-9510834","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":628612888,"identity":"47ecbe3a-0720-4524-ae56-be4c33cd555f","order_by":0,"name":"Abhishree Sinha","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA6ElEQVRIiWNgGAWjYFAC9uO/fxjYABmMDQwQEsLAA3gSpBkK0iCKDxCnhcFAmuHDYQjzAAMR6vn7DyQYFxictzefdrj58wcGG9kNB5jbHuDTInEj8UDyDIPbiXNuJ7ZJHGBIM95wgLHdAK81NxgSDvAY3E6QkE5sAzrscCJQS5sEPh3y5w8YNvAYnLMHamn+cIDhP2EtBkCvMPMYHGCcIZ3YAHTYAcJaDG/kpDHOMEhOBGppkzhjkGw88zABLXLnjx9j+PDHDuiw9McfKirsZPuOtz/DqwXdnUDMTIL6UTAKRsEoGAXYAQByDU+OvrbOfgAAAABJRU5ErkJggg==","orcid":"","institution":"SRM Institute of Science and Technology","correspondingAuthor":true,"prefix":"","firstName":"Abhishree","middleName":"","lastName":"Sinha","suffix":""}],"badges":[],"createdAt":"2026-04-23 23:32:16","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-9510834/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9510834/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":107869923,"identity":"ebbd9c59-7431-4a58-a045-c11722646e08","added_by":"auto","created_at":"2026-04-27 07:38:28","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":376821,"visible":true,"origin":"","legend":"","description":"","filename":"E2EEmbedDetectorResearchpaper.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9510834/v1_covered_065b02fb-6a22-418f-b7e1-802763226785.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eE2E-EmbedDetector: A Lightweight Entity-Embedding Model for Ethereum Phishing Detection\u003c/strong\u003e\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"SRM Institute of Science and Technology","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":"phishing detection, blockchain security, Ethereum transaction, entity embeddings, neural networks","lastPublishedDoi":"10.21203/rs.3.rs-9510834/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9510834/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003ePhishing attacks pose a significant security issue in Ethereum-based blockchain systems. 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