Adaptive Behavior-Based Ransomware Detection via Dynamic Flow Signatures

Adaptive Behavior-Based Ransomware Detection via Dynamic Flow Signatures | 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 Adaptive Behavior-Based Ransomware Detection via Dynamic Flow Signatures Pedro Loco, Sebastian Alonso, George Hartmann, James Whitmore, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5317374/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 continues to pose a significant threat to both individuals and organizations, evolving with sophisticated tactics that bypass traditional detection methods. The introduction of Adaptive Behavior-Based Ransomware Detection (ABRD) presents a dynamic solution capable of identifying ransomware through real-time behavioral analysis, bypassing the limitations of signature-based techniques. ABRD operates through the extraction of flow signatures, capturing operational characteristics of ransomware attacks, which allows for the detection of novel and zero-day variants without relying on predefined signatures. The system utilizes machine learning models to analyze these behavioral patterns, continuously adapting to emerging threats and ensuring high detection accuracy. Experimental evaluations demonstrated ABRD's effectiveness in handling encrypted communications, minimizing false positives, and scaling efficiently across network environments. Its ability to detect ransomware in real-time, combined with adaptive learning capabilities, positions ABRD as a powerful tool for automating cybersecurity defenses and addressing the challenges posed by the constantly evolving landscape of ransomware attacks. ransomware detection flow signatures machine learning zero-day threats 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-5317374","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":369439380,"identity":"bc3fd52a-6ba2-4b54-9b13-f43f9068983b","order_by":0,"name":"Pedro Loco","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA3UlEQVRIiWNgGAWjYHACAwaGAiDF3sBGihYgYuA5DNYiARY7QJQWiWQitZizN2/78MHARp5f8v2xxzwVNnXmDLwHH3/Ao8Wy51jxzBkGaYYzZyezG/OcSZOwbOBLNsBni8GNHGNmHoPDCQa3k9mkedsOSxgc4DGTwKvl/huolpuHgVr+gbWY/8BvCw9Uyw1moJYGiC14vW/Zk1bMCPZLT7KZ5JxjaZI7m3mMJc7g0WLOfngzw4cKYIixH3wm8abGht+cvcfwQwU+h2GKMONRjkMLAR2jYBSMglEw8gAAxKNFqh+RKrEAAAAASUVORK5CYII=","orcid":"https://orcid.org/0009-0001-4994-8041","institution":"","correspondingAuthor":true,"prefix":"","firstName":"Pedro","middleName":"","lastName":"Loco","suffix":""},{"id":369439381,"identity":"68961c33-e0f3-49e3-8926-b055a7793a47","order_by":1,"name":"Sebastian Alonso","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Sebastian","middleName":"","lastName":"Alonso","suffix":""},{"id":369439382,"identity":"e0c3ddad-4f6a-470f-82ec-ffe97199ada8","order_by":2,"name":"George Hartmann","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"George","middleName":"","lastName":"Hartmann","suffix":""},{"id":369439383,"identity":"a06fca29-b323-4e08-b6ce-c3a356d706a1","order_by":3,"name":"James Whitmore","email":"","orcid":"https://orcid.org/0009-0001-1304-2272","institution":"","correspondingAuthor":false,"prefix":"","firstName":"James","middleName":"","lastName":"Whitmore","suffix":""},{"id":369439384,"identity":"8541c964-c651-4994-b07a-7e7675142315","order_by":4,"name":"Edward McLaughlin","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Edward","middleName":"","lastName":"McLaughlin","suffix":""}],"badges":[],"createdAt":"2024-10-23 09:00:14","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-5317374/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5317374/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":67352487,"identity":"1a45bf30-f58d-4c95-919b-0df599e7626d","added_by":"auto","created_at":"2024-10-24 04:22:05","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":213196,"visible":true,"origin":"","legend":"","description":"","filename":"b331.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5317374/v1_covered_be9cdf81-73e4-4f2a-b398-db48ab0bf9f8.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eAdaptive Behavior-Based Ransomware Detection via Dynamic Flow Signatures\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, flow signatures, machine learning, zero-day threats","lastPublishedDoi":"10.21203/rs.3.rs-5317374/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5317374/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eRansomware continues to pose a significant threat to both individuals and organizations, evolving with sophisticated tactics that bypass traditional detection methods. 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