Safe Blood-IoT: A Secure and Intelligent Anomaly Detection Framework for Cold-Chain Preservation

Safe Blood-IoT: A Secure and Intelligent Anomaly Detection Framework for Cold-Chain Preservation | 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 Safe Blood-IoT: A Secure and Intelligent Anomaly Detection Framework for Cold-Chain Preservation P Pal, R Bhattacharya This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8695009/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 This paper presents a security-integrated anomaly detection framework for IoT-enabled blood bank cold chain logistics. Blood supply integrity depends on maintaining strict environmental conditions during storage and transport, which are monitored using IoT sensors. These systems are vulnerable to both operational anomalies (e.g., temperature, humidity, geofence breaches) and cyberattacks that manipulate telemetry (e.g., spoofing, replay, suppression). Existing methods like Isolation Forests and risk fusion models detect statistical outliers or weight anomalies using Common Vulnerability Scoring System (CVSS) / Exploit Prediction Scoring System (EPSS) / Device Vulnerability Density (DVD) scores, but fail to capture malicious data manipulations and often raise false positives. We propose a risk-aware lightweight, layered anomaly detection framework that integrates per-feature temporal detectors, a cyberattack detector based on cross-sensor consistency, and a fusion module incorporating device vulnerability scores. Using a simulated IoT dataset, we demonstrate improved anomaly detection, reduced false positives through temporal persistence, and the ability to flag malicious manipulations. The proposed framework advances IoT cold-chain monitoring with a security-aware and explainable approach. Blood bank cold chain IoT-enabled monitoring Anomaly detection Cyberattack detection Risk-aware Risk-based device vulnerability scoring Isolation Forest CVSS EPSS DVD Full Text Additional Declarations The authors declare no competing interests. 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. 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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-8695009","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":580175736,"identity":"6cdaaaff-e1dd-4fb2-a37c-b022a03d16e0","order_by":0,"name":"P Pal","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA40lEQVRIiWNgGAWjYJACZhDBxsDA+CCBFC0SbGwMzAakaQFawyZBlHJz6cOPPxfusKvjk+89VvGgwkaOgb338Qt8Wiz70sykZ55JBjqML+1Gwpk0Ywae42YW+LQYnGEwY+ZtYwZq4TG7kdh2OLFBIo3NAL8W9s+fedvqwVoKEv/9rydCC4+BNG/bYbAWhsSGAwkMEmnMD/D6pYenDKjluGQbW16yRMKxZMM2nmNs+HQwmPOwbwY6rJpfvvnswY8/auzk+dnbmD/gdRiCyQOhgFbgjyBMLUCA35ZRMApGwSgYcQAANpI88xoOGpkAAAAASUVORK5CYII=","orcid":"","institution":"","correspondingAuthor":true,"prefix":"","firstName":"P","middleName":"","lastName":"Pal","suffix":""},{"id":580176004,"identity":"99ca95ea-9718-4c02-bf7b-2d354c6a1955","order_by":1,"name":"R Bhattacharya","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"R","middleName":"","lastName":"Bhattacharya","suffix":""}],"badges":[],"createdAt":"2026-01-25 22:01:36","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-8695009/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8695009/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":101234316,"identity":"f66ce440-f346-4449-b9f2-aaa164109936","added_by":"auto","created_at":"2026-01-27 14:20:34","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":875604,"visible":true,"origin":"","legend":"","description":"","filename":"ppRBcoldchainpaper1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8695009/v1_covered_0f3eb082-b87d-469f-8ee7-28ea3e769c40.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eSafe Blood-IoT: A Secure and Intelligent Anomaly Detection Framework for Cold-Chain Preservation \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":"Blood bank cold chain, IoT-enabled monitoring, Anomaly detection, Cyberattack detection, Risk-aware, Risk-based device vulnerability scoring, Isolation Forest, CVSS, EPSS, DVD","lastPublishedDoi":"10.21203/rs.3.rs-8695009/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8695009/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis paper presents a security-integrated anomaly detection framework for IoT-enabled blood bank cold chain logistics. 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