Advanced Ransomware Detection: Unveiling Anti-Analysis Tactics through Enhanced Temporal Data Correlation

Advanced Ransomware Detection: Unveiling Anti-Analysis Tactics through Enhanced Temporal Data Correlation | 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 Ransomware Detection: Unveiling Anti-Analysis Tactics through Enhanced Temporal Data Correlation Muhammad Arslan Aftab, Dr. Qaisar Shafi This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4019125/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 persistent threat to computer systems, leading to substantial data breaches and financial losses. Detecting advanced ransomware attacks that employ anti-analysis techniques presents a significant challenge for existing technologies, leaving systems vulnerable. In response to this critical cybersecurity gap, this research endeavors to enhance ransomware detection by effectively countering anti-analysis measures. Acknowledging the limitations of current methods, particularly in the context of dynamic and polymorphic malware, this study directs its focus towards the crucial pre-encryption phase of ransomware attacks. Herein lies the novel contribution of our approach: leveraging enhanced temporal data correlation to identify ransomware before encryption by meticulously analyzing timestamps and detecting advanced analysis evasion techniques such as API hooking and dynamic code variations. Using a range of conventional machine learning classifiers including K Nearest Neighbor, Random Forest, Logistic Regression, SVM, and Decision Tree, we evaluate the efficacy of enriched features for early detection. Our results demonstrate superior performance, notably achieving a remarkable accuracy of 0.98 with the SVM classifier. Evaluation metrics such as recall, precision, and F1 score corroborate the effectiveness of our methodology in detecting anti-analysis ransomware. This research underscores the paramount importance of dynamic analysis in enhancing accuracy and resilience, particularly in the face of evolving ransomware strains. It highlights the necessity for ongoing research to refine feature extraction techniques and explore advanced machine-learning strategies. By addressing a critical component of cybersecurity challenges, our methodology represents a significant step towards the early detection of anti-analysis ransomware in today’s dynamic threat landscape. Computer Architecture and Engineering Anti-Analysis Ransomware Pre-Encryption Detection Advance Analysis Cryptographic API Calls Full Text Additional Declarations The authors declare no competing interests. Supplementary Files snvancouver.bst 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-4019125","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":276638256,"identity":"2334e987-eb0c-46c3-9402-c0ea21ee601e","order_by":0,"name":"Muhammad Arslan Aftab","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA2UlEQVRIiWNgGAWjYBACNvbmgw8+/pGQA/N4gFiCoBaeY8mGMxtsjInXwiCRYybN25CW2EC0Fj6GBANp3h2H07dLJDA+eNvGkCfZQMhhDAcSDOeeOZy7c0YCs+HcNoZiaUK2sDE2HEh4w3Y4d8ONBDZp3jaGxHkEtTAD9fCwHU43uJHA/ps4LUA9jbxtaQlALWzMIC2zCWrhAeqZccbGcMOZh82Sc85JFBP0vvz8999/fKiQkDc4nnzww5symzyJA4SsQQBGkPESCcRrgAEytIyCUTAKRsFwBwBjVz/dCC59TgAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0009-0003-2170-4154","institution":"National University of Computing and Emerging Sciences Islamabad Pakistan","correspondingAuthor":true,"prefix":"","firstName":"Muhammad","middleName":"Arslan","lastName":"Aftab","suffix":""},{"id":276638351,"identity":"73fdc6d7-97e9-405d-82d5-48f009b5a8e8","order_by":1,"name":"Dr. Qaisar Shafi","email":"","orcid":"","institution":"National University of Computing and Emerging Sciences Islamabad Pakistan","correspondingAuthor":false,"prefix":"Dr.","firstName":"Qaisar","middleName":"","lastName":"Shafi","suffix":""}],"badges":[],"createdAt":"2024-03-06 03:23:03","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-4019125/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4019125/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":52126154,"identity":"1d0c8618-7a11-4a76-bd1d-cb5e564faa31","added_by":"auto","created_at":"2024-03-07 06:35:27","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":529362,"visible":true,"origin":"","legend":"","description":"","filename":"finalarticle.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4019125/v1_covered_0c517367-3e3b-4b59-a2e8-f177876231f3.pdf"},{"id":52125522,"identity":"e4ed1ec8-0a42-46f9-8ccd-a0276b9ef636","added_by":"auto","created_at":"2024-03-07 06:27:22","extension":"bst","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":41304,"visible":true,"origin":"","legend":"","description":"","filename":"snvancouver.bst","url":"https://assets-eu.researchsquare.com/files/rs-4019125/v1/695d4c5ce8a2b61c2fd357c0.bst"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eAdvanced Ransomware Detection: Unveiling Anti-Analysis Tactics through Enhanced Temporal Data Correlation\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"National University for computing and emerging sciences","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":"Anti-Analysis Ransomware, Pre-Encryption Detection, Advance Analysis, Cryptographic API Calls","lastPublishedDoi":"10.21203/rs.3.rs-4019125/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4019125/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eRansomware continues to pose a persistent threat to computer systems, leading to substantial data breaches and financial losses. 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