Machine Learning–Based Cryptographic Selection System for IoT Devices Based on Computational Resources

Machine Learning–Based Cryptographic Selection System for IoT Devices Based on Computational Resources | 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 Machine Learning–Based Cryptographic Selection System for IoT Devices Based on Computational Resources Qi Ming Ng, Julia Juremi, Nor Azlina Abd Rahman, Vinesh Thiruchelvam This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8409896/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 rapid expansion of the Internet of Things (IoT) increases the need for secure, efficient data protection in resource-constrained devices. Choosing cryptographic algorithms is challenging. Conventional methods degrade performance; lightweight algorithms may lack security. This research introduces a supervised machine learning system that autonomously recommends optimal encryption methods based on device capabilities. A large-scale data-generation framework simulated 16 symmetric and hybrid cryptographic algorithms on varied IoT devices. Algorithm suitability was scored using weighted metrics: execution time, RAM and ROM use, and battery consumption. This dataset was used to train machine learning models; Random Forest performed best. Further optimization used Sequential Backward Selection (SBS), Synthetic Minority Oversampling Technique (SMOTE), and hyperparameter tuning. The resulting model achieved near-perfect accuracy, F1 score, Matthews correlation coefficient (MCC), and area under the receiver operating characteristic curve (AUC-ROC). The optimized model delivers reliable, context-aware cryptographic recommendations without manual evaluation, helping IoT designers deploy resource-efficient security. The study is limited by simulation-based measurements and by the exclusion of post-quantum algorithms. However, it provides a scalable basis for intelligent selection of cryptographic algorithms in future IoT environments. Internet of Things (IoT) Cryptographic Algorithm Selection Lightweight Cryptography Machine Learning Random Forest Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 03 Mar, 2026 Reviews received at journal 16 Feb, 2026 Reviewers agreed at journal 13 Feb, 2026 Reviewers agreed at journal 08 Feb, 2026 Reviews received at journal 28 Jan, 2026 Reviews received at journal 22 Jan, 2026 Reviewers agreed at journal 21 Jan, 2026 Reviewers agreed at journal 21 Jan, 2026 Reviewers agreed at journal 18 Jan, 2026 Reviews received at journal 18 Jan, 2026 Reviewers agreed at journal 18 Jan, 2026 Reviewers agreed at journal 16 Jan, 2026 Reviewers invited by journal 16 Jan, 2026 Editor invited by journal 26 Dec, 2025 Editor assigned by journal 23 Dec, 2025 Submission checks completed at journal 23 Dec, 2025 First submitted to journal 20 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. 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. 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-8409896","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":576901667,"identity":"3381eef6-bc2e-4e05-aab3-656a0b57aac8","order_by":0,"name":"Qi Ming 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Choosing cryptographic algorithms is challenging. Conventional methods degrade performance; lightweight algorithms may lack security. This research introduces a supervised machine learning system that autonomously recommends optimal encryption methods based on device capabilities. A large-scale data-generation framework simulated 16 symmetric and hybrid cryptographic algorithms on varied IoT devices. Algorithm suitability was scored using weighted metrics: execution time, RAM and ROM use, and battery consumption. This dataset was used to train machine learning models; Random Forest performed best. Further optimization used Sequential Backward Selection (SBS), Synthetic Minority Oversampling Technique (SMOTE), and hyperparameter tuning. The resulting model achieved near-perfect accuracy, F1 score, Matthews correlation coefficient (MCC), and area under the receiver operating characteristic curve (AUC-ROC). 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