Novel Approach for Enhanced Ransomware Detection: Introducing Adaptive Pattern Signature Analysis

Novel Approach for Enhanced Ransomware Detection: Introducing Adaptive Pattern 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 Novel Approach for Enhanced Ransomware Detection: Introducing Adaptive Pattern Signature Analysis Frances Gromov, Julian Ferreira, Patrick Lombardi, Samuel Grigori This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5414506/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 and frequency of ransomware attacks have rendered traditional detection methods increasingly inadequate, necessitating the development of more adaptive and intelligent security solutions. Adaptive Pattern Signature Analysis (APSA) emerges as a novel framework that dynamically generates and adjusts behavioral signatures in real-time, thereby enhancing detection accuracy and adaptability to evolving ransomware tactics. APSA's architecture integrates advanced pattern recognition techniques with adaptive matching mechanisms, enabling the system to identify and respond to previously unseen ransomware behaviors with minimal latency. Comprehensive evaluations demonstrate APSA's superior performance in detection accuracy, reduced false positive rates, and scalability across diverse operational environments. These findings demonstrate APSA's potential to significantly advance ransomware detection technologies, offering a robust and flexible approach to mitigating the impact of sophisticated cyber threats. ransomware detection adaptive pattern recognition behavioral signatures real-time detection 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-5414506","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":375621262,"identity":"a8ccf5e6-d1f5-4d57-9477-b91ca67a82bc","order_by":0,"name":"Frances Gromov","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5ElEQVRIiWNgGAWjYPACOTkow4JoLcbGDAzMIIYE8VoSG4jWIh99+OiGj20G6dvZ+49JfNwhIW9wgPnhoxt4tBieS0u7ObPNIHdnz2E2yZlnJAw3HGAzNs7Bp6WHx+w2z5k/uRtuJDMb87ZJMG44wMMmTYQWg3SD+4+Zjf+2SdgT1CLPA9JSYZBgcIOZ8TFjm0QiQS0GPGxpN2dUGBhuOJNs+LD3jETyzMME/CLfw3zsxgcDA3mD4wcfHPi5w8a273jzw8d4bTmAzGNsYGBQOIxHOdiWBnQtqCKjYBSMglEwChgYACaWTdeZ7hGXAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0009-0005-4351-652X","institution":"","correspondingAuthor":true,"prefix":"","firstName":"Frances","middleName":"","lastName":"Gromov","suffix":""},{"id":375621263,"identity":"3e720dc7-4025-4b09-9f8e-e09e437bc641","order_by":1,"name":"Julian Ferreira","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Julian","middleName":"","lastName":"Ferreira","suffix":""},{"id":375621264,"identity":"a5fbb928-9984-4c57-856b-2ca497e9034c","order_by":2,"name":"Patrick Lombardi","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Patrick","middleName":"","lastName":"Lombardi","suffix":""},{"id":375621265,"identity":"6daacaa2-1bfa-44ae-bdd2-c859af163afb","order_by":3,"name":"Samuel Grigori","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Samuel","middleName":"","lastName":"Grigori","suffix":""}],"badges":[],"createdAt":"2024-11-08 07:35:32","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-5414506/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5414506/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":68677217,"identity":"a7268fb5-2cd8-49a8-a8db-4956c04abdf2","added_by":"auto","created_at":"2024-11-11 02:40:36","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":185128,"visible":true,"origin":"","legend":"","description":"","filename":"researchRansomware.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5414506/v1_covered_3db1eb90-a005-4151-95c3-721600a36592.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eNovel Approach for Enhanced Ransomware Detection: Introducing Adaptive Pattern 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, adaptive pattern recognition, behavioral signatures, real-time detection, machine learning","lastPublishedDoi":"10.21203/rs.3.rs-5414506/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5414506/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe escalating sophistication and frequency of ransomware attacks have rendered traditional detection methods increasingly inadequate, necessitating the development of more adaptive and intelligent security solutions. 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