Privacy-Aware Ransomware Detection in Cloud Environments Using a Hybrid ReLU- GRU, Adaptive Ant Colony Optimization, and Shannon Entropy Framework

Privacy-Aware Ransomware Detection in Cloud Environments Using a Hybrid ReLU- GRU, Adaptive Ant Colony Optimization, and Shannon Entropy Framework | 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 Privacy-Aware Ransomware Detection in Cloud Environments Using a Hybrid ReLU- GRU, Adaptive Ant Colony Optimization, and Shannon Entropy Framework Thanjai Vadivel M, Jagannathan J This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5923913/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 attacks in cloud environments for data storage have an increased concern that demands ransom payments that encrypt critical data. The proposed method is classified based on Shannon Entropy, ReLU-GRU networks and adaptive Ant Colony Optimization (ACO) has been used to enhance the proposed method. The encrypted files are detected utilizing Shanon entropy with file randomness while the sequential data patterns are captured by the ReLU-GRU for the emerging attack patterns for the precise ransom ware classification. The framework is validated using a dataset of 6,245 records , distinguishing ransom ware from benign activity based on system logs, API calls, and process behaviors. Pre-processing techniques, including data normalization, feature extraction, and privacy-preserving API security , refine input data. Results demonstrate 99.13% accuracy, 99.11% precision, 99.07% recall, and a 99.09% F1-score , surpassing methods like SAE-LSTM, GAN, and RLDAC . Shannon Entropy-based API security further prevents unauthorized access. The ROC curve confirms a perfect AUC score of 1.00 , while the model achieves a 99.1% detection rate with a false positive rate of just 0.81% , outperforming techniques like SMO and FeSA . The proposed ReLU-GRU + ACO + Shannon Entropy model proves to be efficient, scalable, and privacy-aware . Future research will explore federated learning for distributed detection and zero-trust security architectures to counter advanced ransomware threats. Ransomware Detection ReLU-GRU Adaptive Ant Colony Optimization(ACO) Shannon Entropy (SE) Cloud Security Deep Learning Intrusion Detection Full Text Additional Declarations No competing interests reported. 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-5923913","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":411392608,"identity":"4eaddd1a-ef86-4e20-b738-25b87a7ebadc","order_by":0,"name":"Thanjai Vadivel M","email":"","orcid":"","institution":"Nandha Engineering College","correspondingAuthor":false,"prefix":"","firstName":"Thanjai","middleName":"Vadivel","lastName":"M","suffix":""},{"id":411392609,"identity":"fb218a24-f99a-410a-8782-26873881d2cb","order_by":1,"name":"Jagannathan J","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABAklEQVRIiWNgGAWjYBACxgYowwBESFQw8CBx8WuRgGg5w8DDQ0gLDEC0MLYxwKzBDZjbj1/8XLjDrs6c/ewBBst5h2XsGZgffmAouIPbYT05xdIzzyRLWPbkJTBIbjsMdBibsQSDwTM8fslJkOZtY5YwOJBjANSSBvKLGdAvh3Fr6X+T/Ju3rV7C4PwboJY5IC3s3/BrmZF+DGjLYQmDGyBbGmyAWngI2DLjDZs1b9txyQ033hgckDgG1HKYp1giAY8Ww/70x7d526r5Dc7nGD6WqJGwZ29v3/jhwx88Whp4ELF2WAJEMgNxAk4NDAzyDOwPEK78gEflKBgFo2AUjFwAAIovShFf8xM5AAAAAElFTkSuQmCC","orcid":"","institution":"Vellore Institute of Technology University","correspondingAuthor":true,"prefix":"","firstName":"Jagannathan","middleName":"","lastName":"J","suffix":""}],"badges":[],"createdAt":"2025-01-29 11:38:23","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5923913/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5923913/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":75812088,"identity":"cb8c9b8a-10d2-44c5-90e6-bd8497dc1c92","added_by":"auto","created_at":"2025-02-08 23:31:21","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":617972,"visible":true,"origin":"","legend":"","description":"","filename":"PrivacyAwareRansomwareDetectionUpdated.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5923913/v1_covered_573bcc36-2e09-4afd-950d-1f7218397575.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Privacy-Aware Ransomware Detection in Cloud Environments Using a Hybrid ReLU- GRU, Adaptive Ant Colony Optimization, and Shannon Entropy Framework","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"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, ReLU-GRU, Adaptive Ant Colony Optimization(ACO), Shannon Entropy (SE), Cloud Security, Deep Learning, Intrusion Detection","lastPublishedDoi":"10.21203/rs.3.rs-5923913/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5923913/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eRansomware attacks in cloud environments for data storage have an increased concern that demands ransom payments that encrypt critical data. The proposed method is classified based on Shannon Entropy, ReLU-GRU networks and adaptive Ant Colony Optimization (ACO) has been used to enhance the proposed method. The encrypted files are detected utilizing Shanon entropy with file randomness while the sequential data patterns are captured by the ReLU-GRU for the emerging attack patterns for the precise ransom ware classification. The framework is validated using a \u003cb\u003edataset of 6,245 records\u003c/b\u003e, distinguishing ransom ware from benign activity based on system logs, API calls, and process behaviors. Pre-processing techniques, including \u003cb\u003edata normalization, feature extraction, and privacy-preserving API security\u003c/b\u003e, refine input data. Results demonstrate \u003cb\u003e99.13% accuracy, 99.11% precision, 99.07% recall, and a 99.09% F1-score\u003c/b\u003e, surpassing methods like \u003cb\u003eSAE-LSTM, GAN, and RLDAC\u003c/b\u003e. \u003cb\u003eShannon Entropy-based API security\u003c/b\u003e further prevents unauthorized access. The \u003cb\u003eROC curve confirms a perfect AUC score of 1.00\u003c/b\u003e, while the model achieves a \u003cb\u003e99.1% detection rate with a false positive rate of just 0.81%\u003c/b\u003e, outperforming techniques like \u003cb\u003eSMO and FeSA\u003c/b\u003e. The \u003cb\u003eproposed ReLU-GRU\u0026thinsp;+\u0026thinsp;ACO\u0026thinsp;+\u0026thinsp;Shannon Entropy model\u003c/b\u003e proves to be \u003cb\u003eefficient, scalable, and privacy-aware\u003c/b\u003e. Future research will explore \u003cb\u003efederated learning for distributed detection\u003c/b\u003e and \u003cb\u003ezero-trust security architectures\u003c/b\u003e to counter advanced ransomware threats.\u003c/p\u003e","manuscriptTitle":"Privacy-Aware Ransomware Detection in Cloud Environments Using a Hybrid ReLU- GRU, Adaptive Ant Colony Optimization, and Shannon Entropy Framework","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-02-06 07:32:28","doi":"10.21203/rs.3.rs-5923913/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","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}}],"origin":"","ownerIdentity":"612b149a-a531-4dac-9028-ade8fc8063b9","owner":[],"postedDate":"February 6th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-02-17T07:38:51+00:00","versionOfRecord":[],"versionCreatedAt":"2025-02-06 07:32:28","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5923913","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5923913","identity":"rs-5923913","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}