NovaSentinel-ChainFed: A Blockchain-Based Secured Federated Deep Learning Framework for Trustworthy IoT Communication

NovaSentinel-ChainFed: A Blockchain-Based Secured Federated Deep Learning Framework for Trustworthy IoT Communication | 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 NovaSentinel-ChainFed: A Blockchain-Based Secured Federated Deep Learning Framework for Trustworthy IoT Communication Sharmila Kumari N, H. S. Vimala, J Shreyas This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8452272/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 17 You are reading this latest preprint version Abstract The proliferation of Internet of Things (IoT) devices has created unprecedented challenges in network security, particularly in detecting and mitigating distributed denial-of-service (DDoS) attacks. Traditional centralized intrusion detection systems face scalability limitations and privacy concerns when deployed across distributed IoT infrastructures. This article introduces \textit{NovaSentinel-ChainFed}, a framework that integrates blockchain-based coordination with federated deep learning to establish trustworthy IoT communication channels. The proposed architecture comprises three synergistic components: (i) the NovaSentinel-ID intrusion detection pipeline employing an ensemble of gradient boosting methods (Random Forest, Extra Trees, Gradient Boosting, XGBoost, and LightGBM) unified through an elastic-net regularized logistic regression meta-learner; (ii) a federated learning simulation across five IoT gateways executing ten communication rounds; and (iii) a blockchain-inspired logging layer for immutable audit trails using SHA-256 hash chaining. Central to the framework is a deep learning-based trust score mechanism that dynamically evaluates client reliability using eight behavioral features, including update norms, cosine similarity with peer updates, local validation performance, attack rate exposure, and historical trust trajectories, thereby enabling adaptive traffic policy decisions. Experimental evaluation on the CICDDoS2019 dataset demonstrates the effectiveness of the framework, achieving an accuracy of $77.08%$, macro-precision of $85.79%$, macro-recall of $84.42%$, a macro F1-score of $85.09%$, and a ROC-AUC of $93.74%$. The system successfully classifies four traffic categories (BENIGN, DrDoS_DNS, DrDoS_LDAP, and DrDoS_MSSQL) with per-class F1-scores ranging from $76%$ to $95%$. The trust mechanism exhibits adaptive behavior across federated rounds, with initial heuristic-based scores converging towards learned behavioral patterns after trust model training. The blockchain layer maintains verified integrity through 11 blocks recording 60 transactions across all rounds. Collectively, these results establish NovaSentinel-ChainFed as a viable solution for secure, privacy-preserving intrusion detection in distributed IoT environments. Federated learning Blockchain Intrusion detection Internet of Things Deep learning Trust mechanism DDoS detection Ensemble learning Stacking Gradient boosting Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 26 Mar, 2026 Reviews received at journal 18 Mar, 2026 Reviewers agreed at journal 18 Mar, 2026 Reviews received at journal 27 Feb, 2026 Reviewers agreed at journal 19 Feb, 2026 Reviews received at journal 18 Feb, 2026 Reviewers agreed at journal 13 Feb, 2026 Reviewers agreed at journal 11 Feb, 2026 Reviewers agreed at journal 30 Jan, 2026 Reviewers agreed at journal 24 Jan, 2026 Reviewers agreed at journal 22 Jan, 2026 Reviewers agreed at journal 22 Jan, 2026 Reviewers invited by journal 22 Jan, 2026 Editor invited by journal 08 Jan, 2026 Editor assigned by journal 29 Dec, 2025 Submission checks completed at journal 29 Dec, 2025 First submitted to journal 26 Dec, 2025 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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Communication","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"discover-artificial-intelligence","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"diai","sideBox":"Learn more about [Discover Artificial Intelligence](https://www.springer.com/44163)","snPcode":"","submissionUrl":"","title":"Discover Artificial Intelligence","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Federated learning, Blockchain, Intrusion detection, Internet of Things, Deep learning, Trust mechanism, DDoS detection, Ensemble learning, Stacking, Gradient boosting","lastPublishedDoi":"10.21203/rs.3.rs-8452272/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8452272/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\nThe proliferation of Internet of Things (IoT) devices has created unprecedented challenges in network security, particularly in detecting and mitigating distributed denial-of-service (DDoS) attacks. Traditional centralized intrusion detection systems face scalability limitations and privacy concerns when deployed across distributed IoT infrastructures. This article introduces \\textit{NovaSentinel-ChainFed}, a framework that integrates blockchain-based coordination with federated deep learning to establish trustworthy IoT communication channels. The proposed architecture comprises three synergistic components: (i) the NovaSentinel-ID intrusion detection pipeline employing an ensemble of gradient boosting methods (Random Forest, Extra Trees, Gradient Boosting, XGBoost, and LightGBM) unified through an elastic-net regularized logistic regression meta-learner; (ii) a federated learning simulation across five IoT gateways executing ten communication rounds; and (iii) a blockchain-inspired logging layer for immutable audit trails using SHA-256 hash chaining. Central to the framework is a deep learning-based trust score mechanism that dynamically evaluates client reliability using eight behavioral features, including update norms, cosine similarity with peer updates, local validation performance, attack rate exposure, and historical trust trajectories, thereby enabling adaptive traffic policy decisions. Experimental evaluation on the CICDDoS2019 dataset demonstrates the effectiveness of the framework, achieving an accuracy of $77.08\\%$, macro-precision of $85.79\\%$, macro-recall of $84.42\\%$, a macro F1-score of $85.09\\%$, and a ROC-AUC of $93.74\\%$. The system successfully classifies four traffic categories (BENIGN, DrDoS\\_DNS, DrDoS\\_LDAP, and DrDoS\\_MSSQL) with per-class F1-scores ranging from $76\\%$ to $95\\%$. The trust mechanism exhibits adaptive behavior across federated rounds, with initial heuristic-based scores converging towards learned behavioral patterns after trust model training. The blockchain layer maintains verified integrity through 11 blocks recording 60 transactions across all rounds. Collectively, these results establish NovaSentinel-ChainFed as a viable solution for secure, privacy-preserving intrusion detection in distributed IoT environments.\n","manuscriptTitle":"NovaSentinel-ChainFed: A Blockchain-Based Secured Federated Deep Learning Framework for Trustworthy IoT Communication","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-24 02:31:45","doi":"10.21203/rs.3.rs-8452272/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-03-26T16:32:01+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-18T07:24:38+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"242183592333981070106364275841772673175","date":"2026-03-18T06:49:32+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-27T08:49:15+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"173842127748229357286025464923025319878","date":"2026-02-19T07:20:32+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-18T08:09:59+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"25916075345464624647678347575979066907","date":"2026-02-13T14:41:08+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"193178367895458519207866317060649468875","date":"2026-02-11T07:47:07+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"75847600313399963480192599710212102027","date":"2026-01-31T04:32:01+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"241242282040115182801826250932005292143","date":"2026-01-24T12:26:06+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"53324135774453255347625037421720888605","date":"2026-01-22T08:10:09+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"116759538344103146617016001840463700352","date":"2026-01-22T07:22:56+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-01-22T07:15:57+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-01-09T04:44:56+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-12-29T09:13:54+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-12-29T09:12:23+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Artificial Intelligence","date":"2025-12-26T05:13:38+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"discover-artificial-intelligence","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"diai","sideBox":"Learn more about [Discover Artificial Intelligence](https://www.springer.com/44163)","snPcode":"","submissionUrl":"","title":"Discover Artificial Intelligence","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"6500071e-7505-4c9b-8c32-bfb781c75c7c","owner":[],"postedDate":"January 24th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-07T05:08:16+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-24 02:31:45","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8452272","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8452272","identity":"rs-8452272","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}