Resilience Evaluation of Memristor-based PUF Against Machine Learning Attacks

Resilience Evaluation of Memristor-based PUF Against Machine Learning Attacks | 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 Resilience Evaluation of Memristor-based PUF Against Machine Learning Attacks Hebatallah M. Ibrahim, Heorhii Skovorodnikov, Hoda Alkhzaimi This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3702385/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 14 Oct, 2024 Read the published version in Scientific Reports → Version 1 posted 10 You are reading this latest preprint version Abstract Physical unclonable functions (PUFs) have emerged as a favorable hardware security primitive, they exploit the process variations to provide unique signatures. CMOS-based PUFs are the most popular type, however, most existing CMOS PUFs are found to be vulnerable to modeling attacks. Memristors leveraging nanotechnology fabrication processes and highly nonlinear behavior became an interesting alternative to the existing CMOS-based PUF technology. Memristor-based PUFs are emerging due to the inherent randomness at both the memristor level due to the cycle-to-cycle (C2C) programming variation of the device. Our study focuses on building a machine learning (ML) analysis and attack framework of tools on Cu / HfO 2-x / p ++ Si memristor-based PUF (MR-PUF). Our objective is to test the resiliency of the security margins of the presented PUF using ML analysis tools. Our main contribution is a holistic study that focuses on attacking the randomness output resiliency based on building randomness predictors using Logistic Regression (LR), Support Vector Machine (SVM), Gaussian Mixture Models (GMM), K-means, K-means++, Random Forest, XGBoost and LSTM, within efficient time, and data complexity. Our results yield low accuracy and ROC results of within 0.49-0.52 and 0.49-0.52 respectively, indicating failure in predicting random data demonstrates efficient randomness prediction resiliency of the MR-PUF. The efficient time and data complexities of these attacks illustrated in this study are yielded to be linear and quadratic resulting in attack execution time in seconds and 5032 training samples combined with 2157 testing samples to verify the randomness of PUF. Physical sciences/Engineering/Electrical and electronic engineering Physical sciences/Mathematics and computing/Computational science Physical sciences/Mathematics and computing/Computer science Physical Unclonable Functions Memristor Modeling Attacks Machine Learning Analysis Full Text Additional Declarations No competing interests reported. Supplementary Files MEMPUFanalysis2.zip Cite Share Download PDF Status: Published Journal Publication published 14 Oct, 2024 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 03 Apr, 2024 Reviews received at journal 21 Mar, 2024 Reviews received at journal 10 Mar, 2024 Reviewers agreed at journal 08 Mar, 2024 Reviewers agreed at journal 07 Mar, 2024 Reviewers invited by journal 15 Jan, 2024 Editor assigned by journal 14 Jan, 2024 Editor invited by journal 11 Jan, 2024 Submission checks completed at journal 11 Jan, 2024 First submitted to journal 03 Dec, 2023 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-3702385","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":267021667,"identity":"3bd1412c-1c70-42ac-bcb9-1541d80b6f24","order_by":0,"name":"Hebatallah M. 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CMOS-based PUFs are the most popular type, however, most existing CMOS PUFs are found to be vulnerable to modeling attacks. Memristors leveraging nanotechnology fabrication processes and highly nonlinear behavior became an interesting alternative to the existing CMOS-based PUF technology. Memristor-based PUFs are emerging due to the inherent randomness at both the memristor level due to the cycle-to-cycle (C2C) programming variation of the device. Our study focuses on building a machine learning (ML) analysis and attack framework of tools on \u003cem\u003eCu\u003c/em\u003e/\u003cem\u003eHfO\u003c/em\u003e\u003csub\u003e2-x\u003c/sub\u003e/\u003cem\u003ep\u003c/em\u003e\u003csup\u003e++\u003c/sup\u003e\u003cem\u003eSi\u003c/em\u003e memristor-based PUF (MR-PUF). Our objective is to test the resiliency of the security margins of the presented PUF using ML analysis tools. 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The efficient time and data complexities of these attacks illustrated in this study are yielded to be linear and quadratic resulting in attack execution time in seconds and 5032 training samples combined with 2157 testing samples to verify the randomness of PUF.\u003c/p\u003e","manuscriptTitle":"Resilience Evaluation of Memristor-based PUF Against Machine Learning Attacks","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-01-15 18:25:41","doi":"10.21203/rs.3.rs-3702385/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-04-03T06:54:06+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-03-21T14:19:13+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-03-10T21:18:54+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"2e19ab1a-099f-4faf-b576-d9096b223403","date":"2024-03-08T14:07:08+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"6653a65e-dca5-48de-b739-d3d8a0456a77","date":"2024-03-07T22:30:10+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-01-15T15:20:46+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-01-14T16:36:11+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-01-11T14:39:10+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-01-11T14:38:16+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2023-12-03T20:03:37+00:00","index":"","fulltext":""}],"status":"published","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}}],"origin":"","ownerIdentity":"0317f7dd-3907-460e-9f15-1361aaafbf41","owner":[],"postedDate":"January 15th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":28129962,"name":"Physical sciences/Engineering/Electrical and electronic engineering"},{"id":28129963,"name":"Physical sciences/Mathematics and computing/Computational science"},{"id":28129964,"name":"Physical sciences/Mathematics and computing/Computer science"}],"tags":[],"updatedAt":"2024-10-21T16:04:20+00:00","versionOfRecord":{"articleIdentity":"rs-3702385","link":"https://doi.org/10.1038/s41598-024-73839-1","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2024-10-14 15:57:58","publishedOnDateReadable":"October 14th, 2024"},"versionCreatedAt":"2024-01-15 18:25:41","video":"","vorDoi":"10.1038/s41598-024-73839-1","vorDoiUrl":"https://doi.org/10.1038/s41598-024-73839-1","workflowStages":[]},"version":"v1","identity":"rs-3702385","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3702385","identity":"rs-3702385","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}