Machine Learning Approaches for Vulnerability Detection in Smart Contracts

Machine Learning Approaches for Vulnerability Detection in Smart Contracts | 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 Machine Learning Approaches for Vulnerability Detection in Smart Contracts Christopher De Baets, Basem Suleiman, Armin Chitizadeh, Imran Razzak This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6691317/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 In the growing field of blockchain technology, smart contracts exist as transformative digital agreements that execute transactions autonomously in decentralised networks. However, these contracts face challenges in the form of security vulnerabilities, posing significant financial and operational risks. While traditional methods to detect and mitigate vulnerabilities in smart contracts are limited due to a lack of comprehensiveness and effectiveness, integrating advanced machine learning technologies presents an attractive approach to increasing effective vulnerability countermeasures. We endeavour to fill an important gap in the literature by conducting a rigorous systematic review, exploring the intersection between machine learning and smart contracts. Specifically, the study examines the potential of machine learning techniques to improve the detection and mitigation of vulnerabilities in smart contracts. We analysed 88 articles published between 2018 and 2023 from the following databases: Institute of Electrical and Electronics Engineers, the Association for Computing Machinery, ScienceDirect, Scopus, and Google Scholar. The findings reveal that classical machine learning techniques, including K-nearest neighbours, random forest, decision trees, extreme gradient boosting, and support vector machines, outperform static tools in vulnerability detection. Moreover, multi-model approaches integrating deep learning and classical machine learning show significant improvements in precision and recall, while hybrid models employing various techniques achieve near-perfect performance in vulnerability detection accuracy. By integrating state-of-the-art solutions, this work synthesises current methods, thoroughly investigates research gaps, and suggests future directions. The insights gathered are intended to serve as a seminal reference for academics, industry experts, and bodies interested in leveraging machine learning to enhance smart contract security. Blockchain Technology Smart Contracts Security Vulnerabilities Machine Learning Vulnerability Detection Systematic Review 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-6691317","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":532521244,"identity":"e3a12fd8-171c-41ec-8fad-d588ee53fa97","order_by":0,"name":"Christopher De Baets","email":"","orcid":"","institution":"The University of Sydney","correspondingAuthor":false,"prefix":"","firstName":"Christopher","middleName":"","lastName":"De Baets","suffix":""},{"id":532521245,"identity":"ca5f5c33-6497-43b0-8295-7dc648f83a88","order_by":1,"name":"Basem Suleiman","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA+ElEQVRIiWNgGAWjYBACAxDxgEECSDIfgIolEKElAayFDaaUOC0gwGNAnBZzBvZnEgkVFtH8s3u+SRf8OczAz55jwPCzDbcWywYeM4mEMxK5M+6c3SY9s+0wg2TPGwPGXjxaDA7wsEkktknkNtzI3SbN23CbweAG0BZevFqADkv8J5E7/0bOM2meP7cZ7IFaGP/i1cJgJpHYIJG74UYOmzQPG9AWiRwDZny2WDbzGFskHJPI3Xgjzdiat+0/j8SZZwWHZc7h1mLO3v7wxoeautx5N5If3ub5kybH35688eGbMtxaGJjR+Dwg4gAeDaNgFIyCUTAKiAAAxx5N5XeDb50AAAAASUVORK5CYII=","orcid":"","institution":"UNSW Sydney","correspondingAuthor":true,"prefix":"","firstName":"Basem","middleName":"","lastName":"Suleiman","suffix":""},{"id":532521246,"identity":"f0ea450e-111c-452c-a7c9-cfba44eb1f97","order_by":2,"name":"Armin Chitizadeh","email":"","orcid":"","institution":"The University of Sydney","correspondingAuthor":false,"prefix":"","firstName":"Armin","middleName":"","lastName":"Chitizadeh","suffix":""},{"id":532521247,"identity":"50c0f1a5-a92e-4aff-a6a1-150764862732","order_by":3,"name":"Imran Razzak","email":"","orcid":"","institution":"UNSW Sydney","correspondingAuthor":false,"prefix":"","firstName":"Imran","middleName":"","lastName":"Razzak","suffix":""}],"badges":[],"createdAt":"2025-05-18 11:08:07","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6691317/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6691317/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":94058292,"identity":"871c1dd3-73c9-4b75-a6bd-d46dbf5ec6ff","added_by":"auto","created_at":"2025-10-22 05:37:59","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1681805,"visible":true,"origin":"","legend":"","description":"","filename":"nqzmmmjsscfqpkxyyrxbtdvhhtgxytsf.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6691317/v1_covered_026f5f72-7019-47d6-aedc-6f6934bf5180.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Machine Learning Approaches for Vulnerability Detection in Smart Contracts","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"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":"Blockchain Technology, Smart Contracts, Security Vulnerabilities, Machine Learning, Vulnerability Detection, Systematic Review","lastPublishedDoi":"10.21203/rs.3.rs-6691317/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6691317/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"In the growing field of blockchain technology, smart contracts exist as transformative digital agreements that execute transactions autonomously in decentralised networks. However, these contracts face challenges in the form of security vulnerabilities, posing significant financial and operational risks. While traditional methods to detect and mitigate vulnerabilities in smart contracts are limited due to a lack of comprehensiveness and effectiveness, integrating advanced machine learning technologies presents an attractive approach to increasing effective vulnerability countermeasures. We endeavour to fill an important gap in the literature by conducting a rigorous systematic review, exploring the intersection between machine learning and smart contracts. Specifically, the study examines the potential of machine learning techniques to improve the detection and mitigation of vulnerabilities in smart contracts. We analysed 88 articles published between 2018 and 2023 from the following databases: Institute of Electrical and Electronics Engineers, the Association for Computing Machinery, ScienceDirect, Scopus, and Google Scholar. 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