Design and Evaluation of a Security-Integrated Anomaly Detection Framework for IoT-Based Blood Bank Cold Chain Kits

Design and Evaluation of a Security-Integrated Anomaly Detection Framework for IoT-Based Blood Bank Cold Chain Kits | 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 Design and Evaluation of a Security-Integrated Anomaly Detection Framework for IoT-Based Blood Bank Cold Chain Kits Sai Harshit Balaji, Arun Kumar B R This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8756732/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. Artificial Intelligence and Machine Learning Systems and Networking Artificial Intelligence and Machine Learning Systems and Networking Artificial Intelligence and Machine Learning Systems and Networking 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 potential competing interests as follows: The authors declare a non-financial competing interest in relation to this preprint. An unauthorized version of this manuscript has been posted elsewhere under incorrect authorship. This preprint is provided to establish original authorship while the matter is under review. The authors declare no financial 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. 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. 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