SAFNet-IoT: Secure Adaptive and Cloud supported Federated Network for IoT-Based Industrial Anomaly Detection

SAFNet-IoT: Secure Adaptive and Cloud supported Federated Network for IoT-Based Industrial Anomaly 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 SAFNet-IoT: Secure Adaptive and Cloud supported Federated Network for IoT-Based Industrial Anomaly Detection Rohit Sharma, M Batumalay, Sunil Kumar, Kahkashan Kouser This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6376809/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 It is a great need to enhance the robust, secure and efficient anomaly detection mechanisms in the context of fast expanding Industrial Internet of Things (IIoT) systems. Over the years, numbers of traditional centralized models have faced lots of challenges such as risks to data privacy, high communication overhead, and vulnerability to adversarial attacks. However, 1 there are limitations which we address with the new federated learning framework termed as SAFNet-IoT (Secure Adaptive Federated Network for IoT) that encompasses Blockchain Based Authentication (BBA) and an Adaptive Autoencoder LSTM (AAEL) anomaly detection module. Smart contracts are used in the BBA mechanism to perform the validation and security of the model updates, by preventing adversarial modifications and maintaining the data integrity. At the same time, the AAEL module dynamically adapts to hyperparameters in response to real time feedback, and at a given instant in time optimize the anomaly detection within heterogeneous IIoT environments. We showcase a SER however, that SAFNet IoT achieves 94.7% anomaly detection accuracy, better than these other conventional FL models, i.e., FedAVG-LSTM (93.1%) and Deep Autoencoder (91.2%). Another thing is that the framework reduces communication overhead by 30% with a bandwidth reduction ratio (BRR) of 0.67, which results in greater scal-ability. Moreover, SAFNet-IoT enables 1.4 seconds average local training time and 4.6 seconds system latency, which is computationally efficient for resource-constrained IIoT nodes. By connecting to the blockchain, security is improved and the authentication success rate is 98.5% while 91.2% of malicious updates are detected outperforming traditional secure aggregation methods. This finding shows that SAFNet-IoT is effective in enhancing the anomaly detection, security resilience as well as communication efficiency in IIoT scenarios. Work on future optimization of smart contract execution, minimizing costs of blockchain transaction, and development of SAFNet-IoT into multi-modal data fusion IIoT anomaly detection will follow. Federated learning blockchain-based authentication anomaly detection industrial IoT autoencoder-LSTM secure model aggregation adaptive learning process innovation 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. 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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-6376809","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":451303498,"identity":"500c6f88-1877-4385-a724-df4ea448e2ae","order_by":0,"name":"Rohit Sharma","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA+klEQVRIiWNgGAWjYFACHoYDDBU2PPbtDUC2gRw/AwNj4wGCWg6cSZMx4DkA0mIs2cDA2EBQC8PBlkM2BhIJIDZICwMDXi3m7b0HD39sOMBjLvn24YM3BQYSuu2HgbbU2ETj0iJz5lzCgYM77vBYzk43NpxjYCBhdiYRqOVYWm4DDi0SEjkGBw6eecbDcDuNTZrH4E+d2QGgFsaGwwS0tB3mYbh5jP03D8iW8w+J1GJwg42NGazlBiFbeM4YHDhzJo1HsieNWRLslxtAWxLw+YW9x/hDRYWNPT/7McYPb/6AHJb+8MGHGhucWnCABNKUj4JRMApGwShAAwAANGSVtvDctgAAAABJRU5ErkJggg==","orcid":"","institution":"Dr. Ambedkar Institute of Technology for Divyangjan","correspondingAuthor":true,"prefix":"","firstName":"Rohit","middleName":"","lastName":"Sharma","suffix":""},{"id":451303499,"identity":"a866889f-9ee6-445e-87b4-08797341e7c1","order_by":1,"name":"M Batumalay","email":"","orcid":"","institution":"INTI International University","correspondingAuthor":false,"prefix":"","firstName":"M","middleName":"","lastName":"Batumalay","suffix":""},{"id":451303500,"identity":"101f9bc2-238d-46b7-9281-d4291e7baf70","order_by":2,"name":"Sunil Kumar","email":"","orcid":"","institution":"Ajay Kumar Garg Engineering College","correspondingAuthor":false,"prefix":"","firstName":"Sunil","middleName":"","lastName":"Kumar","suffix":""},{"id":451303501,"identity":"cfd22b89-2163-421d-803e-e52e96f93216","order_by":3,"name":"Kahkashan Kouser","email":"","orcid":"","institution":"Motihari College of Engineering","correspondingAuthor":false,"prefix":"","firstName":"Kahkashan","middleName":"","lastName":"Kouser","suffix":""}],"badges":[],"createdAt":"2025-04-04 14:08:16","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6376809/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6376809/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":84367927,"identity":"79407144-af69-4b78-b42c-56477c58c629","added_by":"auto","created_at":"2025-06-11 06:31:33","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":6638371,"visible":true,"origin":"","legend":"","description":"","filename":"SAFNetIoTManuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6376809/v1_covered_ffea6366-3927-42aa-9837-485dbe89d7e5.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"SAFNet-IoT: Secure Adaptive and Cloud supported Federated Network for IoT-Based Industrial Anomaly Detection","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":"Federated learning, blockchain-based authentication, anomaly detection, industrial IoT, autoencoder-LSTM, secure model aggregation, adaptive learning, process innovation","lastPublishedDoi":"10.21203/rs.3.rs-6376809/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6376809/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"It is a great need to enhance the robust, secure and efficient anomaly detection mechanisms in the context of fast expanding Industrial Internet of Things (IIoT) systems. Over the years, numbers of traditional centralized models have faced lots of challenges such as risks to data privacy, high communication overhead, and vulnerability to adversarial attacks. However, 1 there are limitations which we address with the new federated learning framework termed as SAFNet-IoT (Secure Adaptive Federated Network for IoT) that encompasses Blockchain Based Authentication (BBA) and an Adaptive Autoencoder LSTM (AAEL) anomaly detection module. Smart contracts are used in the BBA mechanism to perform the validation and security of the model updates, by preventing adversarial modifications and maintaining the data integrity. At the same time, the AAEL module dynamically adapts to hyperparameters in response to real time feedback, and at a given instant in time optimize the anomaly detection within heterogeneous IIoT environments. We showcase a SER however, that SAFNet IoT achieves 94.7% anomaly detection accuracy, better than these other conventional FL models, i.e., FedAVG-LSTM (93.1%) and Deep Autoencoder (91.2%). Another thing is that the framework reduces communication overhead by 30% with a bandwidth reduction ratio (BRR) of 0.67, which results in greater scal-ability. Moreover, SAFNet-IoT enables 1.4 seconds average local training time and 4.6 seconds system latency, which is computationally efficient for resource-constrained IIoT nodes. By connecting to the blockchain, security is improved and the authentication success rate is 98.5% while 91.2% of malicious updates are detected outperforming traditional secure aggregation methods. This finding shows that SAFNet-IoT is effective in enhancing the anomaly detection, security resilience as well as communication efficiency in IIoT scenarios. 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