Enhancing Virtual Physically Unclonable Function Security through Neuron-Criticality Analysis and Lightweight Encryption

Enhancing Virtual Physically Unclonable Function Security through Neuron-Criticality Analysis and Lightweight Encryption | 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 Article Enhancing Virtual Physically Unclonable Function Security through Neuron-Criticality Analysis and Lightweight Encryption Raviha Khan, Hani Saleh, Brahim Mefgouda, Omar Alhussein, Sami Muhaidat This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6831089/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 17 Oct, 2025 Read the published version in Scientific Reports → Version 1 posted 10 You are reading this latest preprint version Abstract Physically Unclonable Functions (PUFs) have long been a key component of hardware-based device authentication. They rely on intrinsic manufacturing variability to give unique and tamper-resistant Identifiers for each silicon device. On the other hand, they have significant limitations, including high hardware overhead, aging degradation, and vulnerability to modeling and side-channel attacks. To address these restrictions, we previously presented Virtual Physically Unclonable Functions (VPUFs), a software-based solution that uses neural networks and split learning to improve scalability, flexibility, and deployment feasibility in resource-constrained Internet of Things (IoT) environments. Despite these advancements, VPUFs remain vulnerable to physical extraction and reverse engineering of the deployed model. In this paper, we present a lightweight, neuron-criticality-aware encryption framework that significantly enhances VPUF security. By conducting detailed ablation analysis, we identify the most critical neurons and selectively apply XOR-based encryption, minimizing computational overhead and preserving authentication accuracy. Coupled with a dynamic key generation mechanism based on Rayleigh fading through Jake’s model, our approach achieves up to 99.4% added security with microsecond-scale latency. Physical sciences/Engineering/Electrical and electronic engineering Physical sciences/Mathematics and computing/Computer science Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 17 Oct, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 10 Jul, 2025 Reviews received at journal 08 Jul, 2025 Reviews received at journal 06 Jul, 2025 Reviewers agreed at journal 29 Jun, 2025 Reviewers agreed at journal 26 Jun, 2025 Reviewers invited by journal 24 Jun, 2025 Editor assigned by journal 16 Jun, 2025 Editor invited by journal 11 Jun, 2025 Submission checks completed at journal 10 Jun, 2025 First submitted to journal 05 Jun, 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. 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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-6831089","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":475838024,"identity":"3bad2ca1-b14a-4de6-81e3-f848607b3f5d","order_by":0,"name":"Raviha Khan","email":"","orcid":"","institution":"Khalifa University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Raviha","middleName":"","lastName":"Khan","suffix":""},{"id":475838025,"identity":"952936bb-4618-454b-92f4-cfc961acc3f6","order_by":1,"name":"Hani Saleh","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA3ElEQVRIie2OMQrCQBBFRwJJM7DtiqJX2JBCbOJVVhZSrdaChVapcgCL3CIXWNkildimFAQrwYCthSMWdhvtLPbB7+bN/wAezx8iKIYyBB7Y3xQEHqrvFXgrmHynTKLD3sAjRTYo7glCOobIGqcyLZbS9HKF/fJQKQQVbzGT7mFGC9PbBiiaRWURAjrXwq0cr4KGbXDW6DMpGwns1rqVhlogtCi4DmiYlcC10yCFWuZ5jbzJkrgUdZzzS9cwnbTtYz1iO3Xm19V6zJg6uWteyM8HgLD73uPxeDxdPAELlD+IZTYsfwAAAABJRU5ErkJggg==","orcid":"","institution":"Khalifa University of Science and Technology","correspondingAuthor":true,"prefix":"","firstName":"Hani","middleName":"","lastName":"Saleh","suffix":""},{"id":475838027,"identity":"23c17086-5ff1-4c1a-a0a4-47a8188b5a5e","order_by":2,"name":"Brahim Mefgouda","email":"","orcid":"","institution":"Khalifa University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Brahim","middleName":"","lastName":"Mefgouda","suffix":""},{"id":475838029,"identity":"9a665321-5f75-4fd7-8797-5e5e6eeba8ce","order_by":3,"name":"Omar Alhussein","email":"","orcid":"","institution":"Khalifa University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Omar","middleName":"","lastName":"Alhussein","suffix":""},{"id":475838030,"identity":"af6c45fa-91d9-4ac0-a16b-a8468764fc48","order_by":4,"name":"Sami Muhaidat","email":"","orcid":"","institution":"Khalifa University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Sami","middleName":"","lastName":"Muhaidat","suffix":""}],"badges":[],"createdAt":"2025-06-05 16:53:18","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6831089/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6831089/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-025-20305-1","type":"published","date":"2025-10-17T15:58:09+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":93956037,"identity":"a9be5044-6040-4823-bf01-ecc766d0557b","added_by":"auto","created_at":"2025-10-20 16:09:34","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1200835,"visible":true,"origin":"","legend":"","description":"","filename":"EnhancingVirtualPhysicallyUnclonableFunctionSecuritythroughNeuronCriticalityAnalysisandLightweightEncryption1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6831089/v1_covered_ca1c370b-f1dc-43dc-a248-1a2dbc12ce24.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Enhancing Virtual Physically Unclonable Function Security through Neuron-Criticality Analysis and Lightweight Encryption","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-6831089/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6831089/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Physically Unclonable Functions (PUFs) have long been a key component of hardware-based device authentication. 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