Secure IOT Framework: Blockchain Authentication and Intrusion Detection 

Secure IOT Framework: Blockchain Authentication and Intrusion Detection | 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 Secure IOT Framework: Blockchain Authentication and Intrusion Detection Kavitha M S, Gayathri Devi K This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6099263/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 Blockchain security leverages decentralized, immutable ledger technology for identity verification and data integrity but faces challenges like high computational overhead, scalability issues, and privacy-explainability trade-offs. To overcome these, this research proposes a framework integrating the Variable Elliptical Anonymous Candidate protocol (VEAQS) with blockchain for secure authentication, ensuring transparency and efficiency. The Quantum Kernel Convoluted Gazelle Self-attention model (QKCGS) enhances intrusion detection by leveraging quantum kernels and self-attention mechanisms. It efficiently handles sequential and contextual data, improving both short- and long-term dependency learning. The model uses quantum gates to regulate past knowledge retention and irrelevant data filtering, boosting detection accuracy. By integrating a deep learning model with SHAP, the framework enhances the interpretability and explains the ability of intrusion detection deployed in the Internet of Things, improving the detection of complex attack patterns while ensuring transparent decision-making. The model further optimizes authentication processes by minimizing computational overhead and latency through Single Candidate Optimization (SCO), streamlining the key generation process. The Gazelle Optimization Algorithm (GOA) is used to fine-tune the intrusion detection systems, making it more responsive to real-time attacks while maintaining the integrity and security of the authentication process. This combination of deep learning, blockchain security, and explainable Artificial Intelligence (AI) creates a robust, efficient, and transparent security solution for modern Internet of Things systems, enabling secure communication, tamper-proof logging, and real-time attack detection. The proposed methods achieve 99.50% of accuracy, 99.52% precision, and 99.60% F1-score, demonstrating significant improvements over existing methods. Attack Classification Authentication Blockchain 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. 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-6099263","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":466007568,"identity":"f68d867d-b40e-4f58-8661-897a2fe41c10","order_by":0,"name":"Kavitha M S","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABBElEQVRIiWNgGAWjYBACCSA+wNgAJJn5Hxz8UAFiMDcQ1nIQpIadh/GxxBmQFkbCWhjAWvh5mA1420BcAlok288YHv64wy5avpn3mITkvNpo/naglh8V23BqkebJMThw8Exy7obDfGkShduO5844zNjA2HPmNk4tcgxpCQcOtjHnbmBmMJOQ3HYstwGohZmxDY8W/mcgLfW585uBWnjnHMudT0iLtETyAaCWw0DDeYwNeBtqgC4koEVyxuMDB862HQeqZEt8LHHsQO5GoJaD+PwicT6x+UNlW3Xu/P7DBw5+qKnLnXf+8MEHPypwa0EHh8HkAaLVA0EdKYpHwSgYBaNghAAAK1pk1pCN4jUAAAAASUVORK5CYII=","orcid":"","institution":"Pollachi Institute of Engineering and Technology","correspondingAuthor":true,"prefix":"","firstName":"Kavitha","middleName":"M","lastName":"S","suffix":""},{"id":466007569,"identity":"3bf1a4b0-48f7-4e06-986a-6606bffd1e11","order_by":1,"name":"Gayathri Devi K","email":"","orcid":"","institution":"Dr. N.G.P Institute of Technology","correspondingAuthor":false,"prefix":"","firstName":"Gayathri","middleName":"Devi","lastName":"K","suffix":""}],"badges":[],"createdAt":"2025-02-24 18:23:23","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6099263/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6099263/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":83902922,"identity":"e086843a-3354-4f80-ac01-ba98db1f24d8","added_by":"auto","created_at":"2025-06-04 09:50:24","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2501941,"visible":true,"origin":"","legend":"","description":"","filename":"ManuscriptWithAuthor.docx.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6099263/v1_covered_c2606df7-2a07-4d40-be8b-1fd2d167702e.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Secure IOT Framework: Blockchain Authentication and Intrusion Detection ","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":"Attack Classification, Authentication, Blockchain, Deep Learning, Intrusion Detection","lastPublishedDoi":"10.21203/rs.3.rs-6099263/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6099263/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Blockchain security leverages decentralized, immutable ledger technology for identity verification and data integrity but faces challenges like high computational overhead, scalability issues, and privacy-explainability trade-offs. 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