Opcode Memory Analysis: A Data-Centric Machine Learning Framework for Early Detection and Attribution of Ransomware

Opcode Memory Analysis: A Data-Centric Machine Learning Framework for Early Detection and Attribution of Ransomware | 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 Opcode Memory Analysis: A Data-Centric Machine Learning Framework for Early Detection and Attribution of Ransomware Benjamin Pesem, James Fairweather, Thomas Pennington This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4941250/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 Ransomware has emerged as one of the most significant threats in the cybersecurity landscape, causing widespread disruption and financial loss across various sectors. To address the growing sophistication of ransomware attacks, a novel machine learning framework leveraging opcode memory analysis has been developed, enabling the early detection and accurate attribution of ransomware. Through the systematic examination of low-level operational instructions within system memory, the proposed model distinguishes itself from traditional approaches by providing a more intrinsic understanding of malware behavior, leading to enhanced detection accuracy and the ability to identify specific ransomware families. The model's architecture, which includes a dual-output mechanism for simultaneous detection and attribution, demonstrates significant scalability and applicability across diverse operational environments. Extensive experimental results indicate that this approach not only surpasses existing methods in terms of detection performance but also offers a robust solution for real-time ransomware threat mitigation. The findings demonstrate the potential of opcode analysis as a critical component in the development of next-generation cybersecurity defenses, contributing to more resilient and proactive protective measures against evolving ransomware threats. Computer Architecture and Engineering opcode analysis ransomware machine learning detection attribution 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. We do this by developing innovative software and high quality services for the global research community. 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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-4941250","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":342382212,"identity":"55916872-75e1-4dd4-bc35-b69b9fc6afa0","order_by":0,"name":"Benjamin Pesem","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA40lEQVRIie3PsQqCQBzH8X8I13LgqgQ9w8mBFvgyLU4XNLZlBLUIrvUWTbVKB7acu4MQFtjSFkRQQ9dadNbWcF+X44cfuAPQ6f62q982gacARH5f1YgCao/T4AcCaNNbcuE+j/XEa87KfY6TxmIqLofhoKBmaMhFQbqRoJR1CsPE2doRpHKtBMlFQUjOUIvhCtnzbGWHhPsEsFxUZHc83hjimOxO1ZckB9eQxCKJQE/i1pJuxGirHwXEDlPPkYRavOYtXnNbntnVH8XAqzK8cyeeTcqD8mLvk6H4/QPR6XQ63UsPPCVQizl6gboAAAAASUVORK5CYII=","orcid":"https://orcid.org/0009-0009-4278-0491","institution":"","correspondingAuthor":true,"prefix":"","firstName":"Benjamin","middleName":"","lastName":"Pesem","suffix":""},{"id":342382213,"identity":"5c0fa0d8-55b5-47be-94a7-7f5651223a47","order_by":1,"name":"James Fairweather","email":"","orcid":"https://orcid.org/0009-0009-7153-3019","institution":"","correspondingAuthor":false,"prefix":"","firstName":"James","middleName":"","lastName":"Fairweather","suffix":""},{"id":342382214,"identity":"70feff88-9c73-4bf0-ae04-07c8677e90a0","order_by":2,"name":"Thomas Pennington","email":"","orcid":"https://orcid.org/0009-0008-3065-532X","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Thomas","middleName":"","lastName":"Pennington","suffix":""}],"badges":[],"createdAt":"2024-08-20 01:31:39","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-4941250/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4941250/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":62904425,"identity":"006e69ea-04b3-4495-8e78-7ecb6febca0e","added_by":"auto","created_at":"2024-08-21 00:35:17","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":198183,"visible":true,"origin":"","legend":"","description":"","filename":"ransom.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4941250/v1_covered_2652de11-dbfb-4af0-a013-fdc173738e81.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eOpcode Memory Analysis: A Data-Centric Machine Learning Framework for Early Detection and Attribution of Ransomware\u003c/p\u003e","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":"opcode analysis, ransomware, machine learning, detection, attribution","lastPublishedDoi":"10.21203/rs.3.rs-4941250/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4941250/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eRansomware has emerged as one of the most significant threats in the cybersecurity landscape, causing widespread disruption and financial loss across various sectors. To address the growing sophistication of ransomware attacks, a novel machine learning framework leveraging opcode memory analysis has been developed, enabling the early detection and accurate attribution of ransomware. Through the systematic examination of low-level operational instructions within system memory, the proposed model distinguishes itself from traditional approaches by providing a more intrinsic understanding of malware behavior, leading to enhanced detection accuracy and the ability to identify specific ransomware families. The model's architecture, which includes a dual-output mechanism for simultaneous detection and attribution, demonstrates significant scalability and applicability across diverse operational environments. Extensive experimental results indicate that this approach not only surpasses existing methods in terms of detection performance but also offers a robust solution for real-time ransomware threat mitigation. 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