Advanced Autonomous Detection of Ransomware Using Dynamic Crypto-Entropy Signature Analysis

Advanced Autonomous Detection of Ransomware Using Dynamic Crypto-Entropy 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 Advanced Autonomous Detection of Ransomware Using Dynamic Crypto-Entropy Signature Analysis Giovanni Prigodichi, Harrison Wainwright, Richard Davis, Jasper Kingsley This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5453009/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 sophistication of cyber threats requires the development of advanced detection mechanisms capable of identifying and mitigating malicious activities with minimal human intervention. The Dynamic Crypto-Entropy Signature Analysis (DCESA) framework introduces an autonomous approach to ransomware detection through the analysis of cryptographic entropy patterns inherent in malicious encryption behaviors. Through dynamically generating unique entropy signatures, DCESA effectively distinguishes between benign and malicious activities, thereby enhancing detection accuracy and reducing false positives. Empirical evaluations have demonstrated DCESA's proficiency in identifying a diverse array of ransomware strains, including previously unseen variants, with minimal impact on system performance. The integration of DCESA into cybersecurity infrastructures offers a proactive and efficient solution for mitigating the impact of ransomware attacks, thereby enhancing the overall security posture of organizations. Computer Architecture and Engineering Entropy Analysis Autonomous Detection Cryptographic Behaviors Ransomware Detection 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. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-5453009","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":378075983,"identity":"3bc17145-ca69-4b98-8e31-bf052048694b","order_by":0,"name":"Giovanni Prigodichi","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA7ElEQVRIiWNgGAWjYFACxgYGBjYJBgNm5mMMDDZyDAwSxGthS2NgSDOGakkgpI2NgcGAgceMOC3m0oebX3wos5A3Z+f59pgnwSDa4HYD24OPP3BrsexLbLOccU7CcGcz73ZjoJbcmXMOsBvOwGOLwRnGNmPeNokEg8O826R5f/zJ7ZdIYJPmIU4LzzNpkC1tIC1/8GtpfgzVwgbWArYFn/ctexjbGEF+2XCYzdxwDsgvMxLbJHvScGsx52F//OFDWZ28wfnDzx68AWrZcCP5mMQPGzwOA8cjKgBFLh4A1ML8Aa+KUTAKRsEoGAUAoLxMn48BHIsAAAAASUVORK5CYII=","orcid":"https://orcid.org/0009-0004-7244-3967","institution":"","correspondingAuthor":true,"prefix":"","firstName":"Giovanni","middleName":"","lastName":"Prigodichi","suffix":""},{"id":378075984,"identity":"53685201-82df-4293-bd0c-a9485df84b05","order_by":1,"name":"Harrison Wainwright","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Harrison","middleName":"","lastName":"Wainwright","suffix":""},{"id":378075985,"identity":"9869381f-f0e6-4be5-a798-d463882f1c38","order_by":2,"name":"Richard Davis","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Richard","middleName":"","lastName":"Davis","suffix":""},{"id":378075986,"identity":"9a454913-a10f-403a-94a7-94fb88e6c96b","order_by":3,"name":"Jasper Kingsley","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Jasper","middleName":"","lastName":"Kingsley","suffix":""}],"badges":[],"createdAt":"2024-11-14 10:12:13","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-5453009/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5453009/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":69042257,"identity":"c2a01d96-9c8c-40a4-b226-9a66504e62d9","added_by":"auto","created_at":"2024-11-15 02:04:46","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":183489,"visible":true,"origin":"","legend":"","description":"","filename":"preprintransomware.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5453009/v1_covered_4f2ae6f2-0a01-4738-a925-f62fee1b3f71.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eAdvanced Autonomous Detection of Ransomware Using Dynamic Crypto-Entropy 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":"Entropy Analysis, Autonomous Detection, Cryptographic Behaviors, Ransomware, Detection","lastPublishedDoi":"10.21203/rs.3.rs-5453009/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5453009/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe sophistication of cyber threats requires the development of advanced detection mechanisms capable of identifying and mitigating malicious activities with minimal human intervention. 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