A Novel Approach of Ransomware Detection with Dynamic Obfuscation Signature Analysis

A Novel Approach of Ransomware Detection with Dynamic Obfuscation Signature Analysis | 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 A Novel Approach of Ransomware Detection with Dynamic Obfuscation Signature Analysis Leli Su, Huapeng Cheng, Lianlian Li, Chicheng Zhang, Yuanyuan Wang, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5375812/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 The exponential rise in ransomware attacks has intensified the demand for sophisticated detection methodologies capable of addressing complex evasion tactics. Dynamic Obfuscation Signature Analysis (DOSA) offers an adaptive, multi-layered framework designed to counter ransomware’s polymorphic transformations through a hybrid approach that combines static analysis, dynamic signature mapping, and machine learning-based adaptation. DOSA’s architecture enhances detection accuracy and operational efficiency, with a modular design that supports real-time processing across file-based, network-based, and memory-based detection layers. Experiments demonstrated that DOSA achieved high accuracy rates, maintaining detection efficacy across a diverse array of ransomware variants by continuously evolving signature profiles based on obfuscation patterns. The framework also exhibited substantial resource efficiency, making it suitable for deployment in diverse environments with varied computational constraints. By providing precise and adaptable threat detection, DOSA contributes a significant advancement to the field of ransomware resilience, offering a robust methodology for preemptive ransomware management within modern cybersecurity infrastructures. ransomware detection obfuscation machine learning 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-5375812","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":373150981,"identity":"eb1d5941-157e-4f1a-9912-8c72d7bbe536","order_by":0,"name":"Leli Su","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA6klEQVRIie3PMWrDMBiGYQXB78U0q0AhvoKCoUsIvYqEwV5s8AE8qBj+bO2a6xiBph7A0A7OktkkEAIpJZpCoSBn7KBnFHrRJ0KC4F+imgyiWT4R+HXmT2aaSGFTuCfwUEKowoeTZKdeB1lDgRGqY998JSRpu1PtSUSvWiHFosLYGl7aw0oDZHznS5hCJgVUyArNSzAzDfEzjf3DthcpaAGsaK/lj3nRMD97E9K7j7tEAsstr9Ao9wp4E/Gxb90wu3J/ydfVm8kQ8pR7h22zbhy/m2QeYfpZns3mnZr9yTvsD5i+EgRBEEy5AfZwRxgrxSUfAAAAAElFTkSuQmCC","orcid":"","institution":"","correspondingAuthor":true,"prefix":"","firstName":"Leli","middleName":"","lastName":"Su","suffix":""},{"id":373150982,"identity":"73d6bc38-db2a-41b0-add7-481c742a5a28","order_by":1,"name":"Huapeng Cheng","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Huapeng","middleName":"","lastName":"Cheng","suffix":""},{"id":373150983,"identity":"8ccc420c-8d34-4a09-bd38-45174273d73c","order_by":2,"name":"Lianlian Li","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Lianlian","middleName":"","lastName":"Li","suffix":""},{"id":373150984,"identity":"09140838-c0f9-4009-add2-2f283aaf2f4f","order_by":3,"name":"Chicheng Zhang","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Chicheng","middleName":"","lastName":"Zhang","suffix":""},{"id":373150985,"identity":"0b5884d7-6d06-4891-88ed-9d42764b66a0","order_by":4,"name":"Yuanyuan Wang","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Yuanyuan","middleName":"","lastName":"Wang","suffix":""},{"id":373150986,"identity":"0edaca81-45a9-403d-91f3-595e5087290c","order_by":5,"name":"Jia Zhao","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Jia","middleName":"","lastName":"Zhao","suffix":""}],"badges":[],"createdAt":"2024-11-02 00:09:35","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-5375812/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5375812/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":68226513,"identity":"bb80d7f9-7fcf-4f80-afe5-8129f57c96f2","added_by":"auto","created_at":"2024-11-05 03:51:04","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":364069,"visible":true,"origin":"","legend":"","description":"","filename":"ransomwaredetection.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5375812/v1_covered_ff901b3a-d649-4355-ae9c-6b8bfb17cd84.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eA Novel Approach of Ransomware Detection with Dynamic Obfuscation Signature Analysis\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":"ransomware, detection, obfuscation, machine learning","lastPublishedDoi":"10.21203/rs.3.rs-5375812/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5375812/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe exponential rise in ransomware attacks has intensified the demand for sophisticated detection methodologies capable of addressing complex evasion tactics. 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