An Efficient Intrusion Detection System and Data Security Using Kcccr-gru and Rfiecc With Blockchain for Iot

An Efficient Intrusion Detection System and Data Security Using Kcccr-gru and Rfiecc With Blockchain for Iot | 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 An Efficient Intrusion Detection System and Data Security Using Kcccr-gru and Rfiecc With Blockchain for Iot Kshitiz Saxena, Dhowmya Bhatt This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6963499/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 Internet of Things (IoT) has transformed numerous sectors by facilitating smart devices for collecting and exchanging data. Nevertheless, owing to insufficient data protection, numerous IoT systems face security risks. To overcome these issues, an efficient intrusion detection system and data security using KCCCR-GRU and RFIECC with Blockchain (BC) for IoT is proposed in this paper. The router details are entered during router registration. By using Unicode Transformation Format 32-Universally Unique Identifier (UTF32-UUID) and Entropy Naor Reingold Pseudo Random Number Generator (ENR-PRNG), the Service Set Identifier (SSID) and router Personal Identification Number (PIN) are generated, respectively. Further, for both router registration and the initialized IoT sensor node, public and private keys are generated. By using Exponential Public Keys-MenezesQu Vanstone (EPK-MQV), a secret key is created. Then, the SSID and PIN are combined by the Exclusive OR (XOR) operation and then hashed with Exclusive NOR (X-NOR) Inversion-HAVAL (XNORI-HAVAL). After that, the resultant hashcode is stored in the BC. Simultaneously, the SSID is stored as nonce in the BC. During transmission, the hashcode of the connected router of the IoT sensor node is verified with the hashcode in the BC. If the verification is successful, then the device is authenticated by utilizing the Exponential Public Keys-MenezesQu Vanstone-Message Authentication Code (EPK-MQV-MAC). Thereafter, by using an IDS, data from the IoT node is collected and tested. The dataset undergoes preprocessing in which features are extracted. These features are selected utilizing Linear Scaling-based Walrus Optimization Algorithm (LS-WaOA) and further classified with Kendall Correlation Coefficient Concatenated Rectified Linear Units Gated Recurrent Unit (KCCCR-GRU). Lastly, using Robust Frobenius Isogenies Elliptic Curve Cryptography (RFIECC), non-attacked data is encrypted. Thus, the proposed approach achieves a high level of data security with 98.16667% accuracy. The outcome of the research in terms of societal benefits include increased security and detection of malicious nodes for a safer computing environment. Unicode Transformation Format 32-Universally Unique Identifier (UTF32-UUID) Entropy Naor Reingold Pseudo Random Number Generator (ENR-PRNG) Exponential Public Keys-MenezesQu Vanstone (EPK-MQV) X-NOR Inversion-HAVAL (XNORI-HAVAL) Linear Scaling based Walrus Optimization Algorithm (LS-WaOA) Kendall Correlation Coefficient Concatenated Rectified Linear Units Gated Recurrent Unit (KCCCR-GRU) Robust Frobenius Isogenies Elliptic curve cryptography (RFIECC) 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-6963499","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":476275984,"identity":"15ebe9a7-1d7c-4d87-bc77-731bfbb45469","order_by":0,"name":"Kshitiz Saxena","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA7UlEQVRIie3OsWsCMRTH8V+XdAlkzSHc3/DgwPX+lYRCXaQ4dhKncym4+mfoIo4pBaeIa7rVQacOji1k6DtxU+/s5pDvEHiQD+8BqdQdpiHhQICqJ4PTa9qIY5KNjp/pNsJrAHLHmdD8n8sm6/ePw2BYFpv1bvYVI9Rjn7BdXicd/WL4MGEXodcNtiJkb98E66+TXEtiIk03CBHsiECBt9iqgShfE10Wk5UIJhLKNtJBvyb0MMMzE8FbdAvJpkw8GTsNTGxVSO33A9dE9MYXh9c4LBUf9vkT81yNn+bb3wZylqwf9w+QSqVSqQv9Af7sUryrWB+LAAAAAElFTkSuQmCC","orcid":"","institution":"SRM Institute of Science \u0026 Technology","correspondingAuthor":true,"prefix":"","firstName":"Kshitiz","middleName":"","lastName":"Saxena","suffix":""},{"id":476275985,"identity":"a58f336e-e7d1-462f-8ac5-576d8eb8dff9","order_by":1,"name":"Dhowmya Bhatt","email":"","orcid":"","institution":"SRM Institute of Science \u0026 Technology","correspondingAuthor":false,"prefix":"","firstName":"Dhowmya","middleName":"","lastName":"Bhatt","suffix":""}],"badges":[],"createdAt":"2025-06-24 08:38:28","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6963499/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6963499/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":98882921,"identity":"166aec99-8f61-4864-a870-ecf50c975b67","added_by":"auto","created_at":"2025-12-23 14:24:58","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":822295,"visible":true,"origin":"","legend":"","description":"","filename":"KS.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6963499/v1_covered_c70bd69c-be6c-47a8-87cb-74f17bf011b5.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eAn Efficient Intrusion Detection System and Data Security Using Kcccr-gru and Rfiecc With Blockchain for Iot\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":"Unicode Transformation Format 32-Universally Unique Identifier (UTF32-UUID), Entropy Naor Reingold Pseudo Random Number Generator (ENR-PRNG), Exponential Public Keys-MenezesQu Vanstone (EPK-MQV), X-NOR Inversion-HAVAL (XNORI-HAVAL), Linear Scaling based Walrus Optimization Algorithm (LS-WaOA), Kendall Correlation Coefficient Concatenated Rectified Linear Units Gated Recurrent Unit (KCCCR-GRU), Robust Frobenius Isogenies Elliptic curve cryptography (RFIECC)","lastPublishedDoi":"10.21203/rs.3.rs-6963499/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6963499/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe Internet of Things (IoT) has transformed numerous sectors by facilitating smart devices for collecting and exchanging data. Nevertheless, owing to insufficient data protection, numerous IoT systems face security risks. To overcome these issues, an efficient intrusion detection system and data security using KCCCR-GRU and RFIECC with Blockchain (BC) for IoT is proposed in this paper. The router details are entered during router registration. By using Unicode Transformation Format 32-Universally Unique Identifier (UTF32-UUID) and Entropy Naor Reingold Pseudo Random Number Generator (ENR-PRNG), the Service Set Identifier (SSID) and router Personal Identification Number (PIN) are generated, respectively. Further, for both router registration and the initialized IoT sensor node, public and private keys are generated. By using Exponential Public Keys-MenezesQu Vanstone (EPK-MQV), a secret key is created. Then, the SSID and PIN are combined by the Exclusive OR (XOR) operation and then hashed with Exclusive NOR (X-NOR) Inversion-HAVAL (XNORI-HAVAL). After that, the resultant hashcode is stored in the BC. Simultaneously, the SSID is stored as nonce in the BC. During transmission, the hashcode of the connected router of the IoT sensor node is verified with the hashcode in the BC. If the verification is successful, then the device is authenticated by utilizing the Exponential Public Keys-MenezesQu Vanstone-Message Authentication Code (EPK-MQV-MAC). Thereafter, by using an IDS, data from the IoT node is collected and tested. The dataset undergoes preprocessing in which features are extracted. These features are selected utilizing Linear Scaling-based Walrus Optimization Algorithm (LS-WaOA) and further classified with Kendall Correlation Coefficient Concatenated Rectified Linear Units Gated Recurrent Unit (KCCCR-GRU). Lastly, using Robust Frobenius Isogenies Elliptic Curve Cryptography (RFIECC), non-attacked data is encrypted. Thus, the proposed approach achieves a high level of data security with 98.16667% accuracy. 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