Ransomware Detection Through Contextual Behavior Mapping and Sequential Dependency Analysis

Ransomware Detection Through Contextual Behavior Mapping and Sequential Dependency 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 Ransomware Detection Through Contextual Behavior Mapping and Sequential Dependency Analysis Nikolina Blaas, Jacob Winterbourne, William Beauregarde, Eliza Heathcote This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5527159/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 escalating sophistication of cyber threats necessitates innovative detection methodologies to safeguard digital infrastructures. This research introduces a novel framework that integrates contextual behavior mapping with sequential dependency analysis, aiming to enhance the identification of both known and emerging ransomware variants. By employing probabilistic modeling and graph-based techniques, the proposed system effectively captures complex operational patterns inherent in ransomware activities. Extensive experiments conducted within controlled environments demonstrate the framework's high detection accuracy and low false positive rates, even when confronted with advanced evasion strategies. The scalability assessment reveals its capability to manage concurrent ransomware instances without significant performance degradation, showing its applicability in large-scale network infrastructures. Furthermore, the resource efficiency analysis indicates minimal computational overhead, facilitating seamless integration into existing security architectures. The robustness evaluation against polymorphic and metamorphic ransomware families highlights the framework's resilience, emphasizing the importance of adaptive detection mechanisms in contemporary cybersecurity landscapes. Collectively, these findings validate the proposed approach as a practical and efficient solution for ransomware detection, addressing critical challenges in modern security environments through innovative analytical methodologies and scalable design. Computer Architecture and Engineering Cybersecurity Machine Learning Behavior Analysis Detection Framework Probabilistic Modeling Graph-Based Techniques 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. 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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-5527159","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":382829395,"identity":"041681bc-f8cc-480f-91d7-fb532ea437c5","order_by":0,"name":"Nikolina Blaas","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA0klEQVRIiWNgGAWjYDACZjZmhsQGCxDrwGeGH8RrkQCy2BJnM/YQZQ1QCyNYC4/hbAY2IjSYs7MlGzzcISHP33/mY3MBTx0Df3t3Al4tls1shxMSz0gYzriRu7F5hsVhBokzZzfg1WJwmL35QGKbBGPDDd7tj3l4DjAYSOQSp8V+/vkzD5t52OqI0QJyWJtE4oYDOYxALcxEaUk2APoleeONNMPmmT2HeQj75fwxY8mfO2xs550//LC54EedHH97L34tGICHNOWjYBSMglEwCrACANieSQP1c0QbAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0009-0001-2550-9589","institution":"","correspondingAuthor":true,"prefix":"","firstName":"Nikolina","middleName":"","lastName":"Blaas","suffix":""},{"id":382829396,"identity":"4108d21d-bea6-46d6-a3c3-7cb0bbac7218","order_by":1,"name":"Jacob Winterbourne","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Jacob","middleName":"","lastName":"Winterbourne","suffix":""},{"id":382829397,"identity":"f3ea5ef9-34ed-4618-8d10-61ebdb9e11ee","order_by":2,"name":"William Beauregarde","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"William","middleName":"","lastName":"Beauregarde","suffix":""},{"id":382829398,"identity":"a665bb69-9c38-4474-99f3-5aa81bd2f0f7","order_by":3,"name":"Eliza Heathcote","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Eliza","middleName":"","lastName":"Heathcote","suffix":""}],"badges":[],"createdAt":"2024-11-26 11:05:39","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-5527159/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5527159/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":69953900,"identity":"3b8edc2a-6d77-471d-a327-8db235a3ea76","added_by":"auto","created_at":"2024-11-27 02:57:01","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":490375,"visible":true,"origin":"","legend":"","description":"","filename":"preprint.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5527159/v1_covered_0768ab94-a69f-4f8e-ad29-a23bd6ec91a9.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eRansomware Detection Through Contextual Behavior Mapping and Sequential Dependency 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":"Cybersecurity, Machine Learning, Behavior Analysis, Detection Framework, Probabilistic Modeling, Graph-Based Techniques","lastPublishedDoi":"10.21203/rs.3.rs-5527159/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5527159/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe escalating sophistication of cyber threats necessitates innovative detection methodologies to safeguard digital infrastructures. This research introduces a novel framework that integrates contextual behavior mapping with sequential dependency analysis, aiming to enhance the identification of both known and emerging ransomware variants. By employing probabilistic modeling and graph-based techniques, the proposed system effectively captures complex operational patterns inherent in ransomware activities. Extensive experiments conducted within controlled environments demonstrate the framework's high detection accuracy and low false positive rates, even when confronted with advanced evasion strategies. The scalability assessment reveals its capability to manage concurrent ransomware instances without significant performance degradation, showing its applicability in large-scale network infrastructures. Furthermore, the resource efficiency analysis indicates minimal computational overhead, facilitating seamless integration into existing security architectures. 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