HGS-RF: Heuristic-Guided Selective Random Forest For Enhancing Digital Forensic Investigations Based On Detecting Vulnerabilities In Smart Contracts

HGS-RF: Heuristic-Guided Selective Random Forest For Enhancing Digital Forensic Investigations Based On Detecting Vulnerabilities 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 HGS-RF: Heuristic-Guided Selective Random Forest For Enhancing Digital Forensic Investigations Based On Detecting Vulnerabilities In Smart Contracts DR.Eng Saad AlAzzam, Ghassan Saleh ALDharhani, Raenu ALKolandaisamy This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8345070/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 12 You are reading this latest preprint version Abstract In recent years, Blockchain technology has stood out and widely spread. Blockchain is a decentralized structure that executes operations and transactions automatically. These automatic operations mainly depend on smart contracts, which are among the most important components of Blockchain. Smart contracts involve a set of business transactions. They can be described in simpler terms as an agreement between two parties, similar to any sales contract that is entered into between two people. Once the pre-defined terms of the contract are met, the contract is automatically verified and executed. The automated execution nature of smart contracts makes them vulnerable to numerous security threats. Attackers try to exploit vulnerabilities within the smart contract to steal funds. This leads to serious financial and operational consequences for companies and institutions that depend on Blockchain networks in their operations. This research proposes the HGS-RF (Heuristic-Guided Selective Random Forest) framework, which is a hybrid model that enhances digital forensic investigations by detecting vulnerabilities in smart contracts. The methodology integrates Natural Language Processing (NLP) for feature extraction using the RoBERTA model. Then it applies a binary classification process using a Multilayer Perceptron (MLP) network to verify if the contract is secure or vulnerable. If a contract is vulnerable, a multi-class classification process is performed. This multi-class classification process is based on the HGS-RF model. The multilayer classification process addresses the critical gap, which is multi-label detection, where a single node contains multiple security vulnerabilities. The MLP achieved a performance with 98–99% F1-score, and the HGS-RF model achieved an overall accuracy of 96.7–97% across datasets with up to 37 vulnerability types. The proposed model aims to improve the accuracy and efficiency of smart contract vulnerability detection and contribute to finding a solution to this problem in line with current security needs and long-term sustainability goals. Artificial Intelligence Digital Forensic Investigation Smart contracts Roberta Vulnerability detection Security analysis Sustainable Digital Infrastructure Sustainable Growth Full Text Additional Declarations No competing interests reported. Supplementary Files MAINInfromatioMEANDDRs.docx Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 01 Apr, 2026 Reviews received at journal 25 Feb, 2026 Reviews received at journal 14 Feb, 2026 Reviewers agreed at journal 10 Feb, 2026 Reviewers agreed at journal 08 Feb, 2026 Reviews received at journal 08 Feb, 2026 Reviewers agreed at journal 07 Feb, 2026 Reviewers agreed at journal 29 Jan, 2026 Reviewers invited by journal 29 Jan, 2026 Editor assigned by journal 24 Dec, 2025 Submission checks completed at journal 24 Dec, 2025 First submitted to journal 12 Dec, 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-8345070","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":582529831,"identity":"5fc24832-23d5-421f-987c-dff45729a3a1","order_by":0,"name":"DR.Eng Saad AlAzzam","email":"data:image/png;base64,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","orcid":"","institution":"UCSI University","correspondingAuthor":true,"prefix":"DR.","firstName":"Eng","middleName":"Saad","lastName":"AlAzzam","suffix":""},{"id":582529832,"identity":"b259154b-2502-4b47-a0aa-52977b07e5d6","order_by":1,"name":"Ghassan Saleh ALDharhani","email":"","orcid":"","institution":"UCSI University","correspondingAuthor":false,"prefix":"","firstName":"Ghassan","middleName":"Saleh","lastName":"ALDharhani","suffix":""},{"id":582529833,"identity":"4074d143-8df9-4efa-a2c3-d36954d9528f","order_by":2,"name":"Raenu ALKolandaisamy","email":"","orcid":"","institution":"UCSI University","correspondingAuthor":false,"prefix":"","firstName":"Raenu","middleName":"","lastName":"ALKolandaisamy","suffix":""}],"badges":[],"createdAt":"2025-12-12 11:08:42","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8345070/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8345070/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":101673725,"identity":"a5acae87-4156-48b4-ad40-8cb476ce82a2","added_by":"auto","created_at":"2026-02-02 13:13:07","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":183538,"visible":true,"origin":"","legend":"","description":"","filename":"MAINInfromatioMEANDDRs.docx","url":"https://assets-eu.researchsquare.com/files/rs-8345070/v1/d38eb11381f06596462ba973.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"HGS-RF: Heuristic-Guided Selective Random Forest For Enhancing Digital Forensic Investigations Based On Detecting Vulnerabilities In Smart Contracts","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"international-journal-of-networked-and-distributed-computing","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [International Journal of Networked and Distributed Computing](https://link.springer.com/journal/44227)","snPcode":"44227","submissionUrl":"https://submission.springernature.com/new-submission/44227/3","title":"International Journal of Networked and Distributed Computing","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Open","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Artificial Intelligence, Digital Forensic Investigation, Smart contracts, Roberta, Vulnerability detection, Security analysis, Sustainable Digital Infrastructure, Sustainable Growth","lastPublishedDoi":"10.21203/rs.3.rs-8345070/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8345070/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIn recent years, Blockchain technology has stood out and widely spread. 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This research proposes the HGS-RF (Heuristic-Guided Selective Random Forest) framework, which is a hybrid model that enhances digital forensic investigations by detecting vulnerabilities in smart contracts. The methodology integrates Natural Language Processing (NLP) for feature extraction using the RoBERTA model. Then it applies a binary classification process using a Multilayer Perceptron (MLP) network to verify if the contract is secure or vulnerable. If a contract is vulnerable, a multi-class classification process is performed. This multi-class classification process is based on the HGS-RF model. The multilayer classification process addresses the critical gap, which is multi-label detection, where a single node contains multiple security vulnerabilities. The MLP achieved a performance with 98\u0026ndash;99% F1-score, and the HGS-RF model achieved an overall accuracy of 96.7\u0026ndash;97% across datasets with up to 37 vulnerability types. The proposed model aims to improve the accuracy and efficiency of smart contract vulnerability detection and contribute to finding a solution to this problem in line with current security needs and long-term sustainability goals.\u003c/p\u003e","manuscriptTitle":"HGS-RF: Heuristic-Guided Selective Random Forest For Enhancing Digital Forensic Investigations Based On Detecting Vulnerabilities In Smart Contracts","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-02 13:11:59","doi":"10.21203/rs.3.rs-8345070/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-04-01T07:12:33+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-25T10:19:26+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-14T10:43:32+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"172240933768800383354596890905783986883","date":"2026-02-10T09:01:32+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"320060609940295465459202923674963188082","date":"2026-02-08T11:50:02+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-08T07:47:42+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"120203756825947664218019456938626151492","date":"2026-02-07T08:35:46+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"23014547149503824449997327726123803541","date":"2026-01-29T14:33:29+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-01-29T11:48:56+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-12-24T05:04:17+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-12-24T05:02:23+00:00","index":"","fulltext":""},{"type":"submitted","content":"International Journal of Networked and Distributed Computing","date":"2025-12-12T10:50:25+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"international-journal-of-networked-and-distributed-computing","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [International Journal of Networked and Distributed Computing](https://link.springer.com/journal/44227)","snPcode":"44227","submissionUrl":"https://submission.springernature.com/new-submission/44227/3","title":"International Journal of Networked and Distributed Computing","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Open","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"4d724381-f992-4bf8-aa52-2f5c1a1ccee1","owner":[],"postedDate":"February 2nd, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[],"tags":[],"updatedAt":"2026-04-01T07:26:04+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-02 13:11:59","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8345070","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8345070","identity":"rs-8345070","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}