Deep Learning–based IDS framework for Cloud Data Security

Deep Learning–based IDS framework for Cloud Data Security | 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 Deep Learning–based IDS framework for Cloud Data Security Bhavna Gangwar, Nupa Ram Chauhan This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8323244/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 Cloud computing's scalable, adaptable, and cost-efficient processing and storage capabilities have completely transformed big data management. However, moving sensitive data into dispersed cloud environments presents significant privacy and security issues, such as advanced persistent threats, data breaches, and illegal access. This paper proposed an integrated framework that integrates access control and a hybrid deep learning and machine learning-based intrusion detection system (IDS) for real-time threat detection to improve cloud data security. The framework includes data preprocessing, advanced SMOTE for balancing classes, and hybrid feature learning using 1D Residual Autoencoders (RAE) and Convolutional Neural Networks (CNN).The experimental set up is processed on benchmark datasets (UNSB-NB15, NSL-KDD, and WSN-DS), which shows that the model can successfully detect both frequent and rare attacks with an accuracy of 97.6%, 95% and 95.68%, respectively.The integration of intelligent intrusion detection with cloud data security ensures multi-layered security, increasing confidentiality, integrity and availability across cloud infrastructure. Future research will focus on integrating federated learning and blockchain-based trust management to enable decentralized, privacy-preserving, and adaptable cloud security solutions. XGBoost LightGBM CatBoost Intrusion Detection System Convolutional Neural Network 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. 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-8323244","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":557999139,"identity":"53567b61-5312-4823-a24e-1cf4373b9b05","order_by":0,"name":"Bhavna Gangwar","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABBElEQVRIiWNgGAWjYBCDBAMQyfjPBkQ2HiBeCwNbGkhLA0laDoMpvFrk3c8efs1Tcy/PnP2M2YMfPOft1rYfBtpSYxONS4vhmbw0a55jxcWWPTnmhj0St5O3nUkEajmWltuAS0tDjplxDltC4oYDaWkSPAa3k80OALUwNhzGraX/DVDLP6CW88/SJP8knEs2O/8QvxZ5iRzjx7ltQC03ko9J8xw4YGd2g4AtBhJvzJj/9iUUG9x4fExatiE5wewG0JYEPH6R788x/jjjW0KewfnENsm3DXb2ZufTHz74UGOD25YDDGwSyAKJYJUJOJSDbWlgYP6ALGCPR/EoGAWjYBSMUAAASxJoTb9DzKMAAAAASUVORK5CYII=","orcid":"","institution":"Teerthanker Mahaveer University","correspondingAuthor":true,"prefix":"","firstName":"Bhavna","middleName":"","lastName":"Gangwar","suffix":""},{"id":557999140,"identity":"86659e08-4cb7-4c6d-a1df-a18fba216b41","order_by":1,"name":"Nupa Ram Chauhan","email":"","orcid":"","institution":"Teerthanker Mahaveer University","correspondingAuthor":false,"prefix":"","firstName":"Nupa","middleName":"Ram","lastName":"Chauhan","suffix":""}],"badges":[],"createdAt":"2025-12-10 05:23:58","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8323244/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8323244/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":97937730,"identity":"44c17157-bc9d-46a7-b38b-97ebc8a8d391","added_by":"auto","created_at":"2025-12-11 02:43:14","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":993788,"visible":true,"origin":"","legend":"","description":"","filename":"IDSPAper.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8323244/v1/febff4765e9b02b0de57231c.pdf"},{"id":97937729,"identity":"87c98ea5-67ab-437a-b608-8bb468f101b9","added_by":"auto","created_at":"2025-12-11 02:43:14","extension":"json","order_by":1,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":3997,"visible":true,"origin":"","legend":"","description":"","filename":"63f20857ea274aee8d1d3a9812898914.json","url":"https://assets-eu.researchsquare.com/files/rs-8323244/v1/0485dd3615404e24f324eba1.json"},{"id":97937732,"identity":"87f7b1b1-627f-4fdb-88ed-63f17ebc4b21","added_by":"auto","created_at":"2025-12-11 02:43:22","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":646167,"visible":true,"origin":"","legend":"","description":"","filename":"IDSPAper.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8323244/v1_covered_aa874cd1-e66d-4899-b732-559533aca9c0.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Deep Learning–based IDS framework for Cloud Data Security","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"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":"XGBoost, LightGBM, CatBoost, Intrusion Detection System, Convolutional Neural Network","lastPublishedDoi":"10.21203/rs.3.rs-8323244/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8323244/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Cloud computing's scalable, adaptable, and cost-efficient processing and storage capabilities have completely transformed big data management. 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