A Hardware Trojan Detection Method Based on Recognizing the Waveform Similarity of Oscillating Circuit

A Hardware Trojan Detection Method Based on Recognizing the Waveform Similarity of Oscillating Circuit | 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 A Hardware Trojan Detection Method Based on Recognizing the Waveform Similarity of Oscillating Circuit Chaoen Xiao, Furong Yu, Lei Zhang, Jianxin Wang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6266839/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 To solve the problem that the hardware Trojan occupies a small number of resources, and the triggering condition is difficult to detect, this paper proposes a Hardware Trojan detection method based on recognizing the waveform similarity of oscillation circuit. First, a model for detecting the hardware Trojan is built. Then, four hardware Trojans aimed at the AES encryption chips in the Trust-Hub library are detected respectively by using a ring oscillation circuit or a multivibrator circuit with external connection. The square wave signals at the feature points are extracted by principal component analysis and the results were obtained by correlation analysis, i.e. recognizing the waveform similarity by calculating the coefficient of association. Experimental results show that the four hardware Trojans could be identified, and one of the four Hardware Trojans only occupies 0.26% of the total chip resources. In addition, compared to the traditional detection methods based on detecting the power consumption, this method has higher accuracy and efficiency for detecting the Hardware Trojans at the inactive state. Physical sciences/Mathematics and computing/Computer science Physical sciences/Mathematics and computing/Information technology Hardware Trojan detection AES Oscillation circuit PCA Correlation analysis Full Text Additional Declarations No competing interests reported. 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. 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-6266839","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":447921758,"identity":"f00d749a-376a-4fab-8911-67bbc1757239","order_by":0,"name":"Chaoen Xiao","email":"","orcid":"","institution":"Beijing Electronic Science and Technology Institute","correspondingAuthor":false,"prefix":"","firstName":"Chaoen","middleName":"","lastName":"Xiao","suffix":""},{"id":447921762,"identity":"4a2a0155-9ed2-4ca4-8c84-1a24d68b0d46","order_by":1,"name":"Furong Yu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA2klEQVRIie2RsQrCMBCGEwpxCbpeUeorRArFzVdplrpUcIwgaKkkg7r3MRxdpdAp7h3bR3BzEa2uiqmbQ775Pu7++xGyWP4Q0kmSCgTxcIqTKhRLs9KlecrGuus7Ks1ZpQuz4sFUwkJ6XO2KyK03TovD6CllpQ7wPosDwdcE9dQ2NGapMxE5bqOU/DhAoM8H4xYfdEFGL0UTxGBmUIDL/k3e6aSMgzmXTjsFXEkAN/FRO+UZHzRhuHkyhLqgxixDpepnlaukqfJyFUuvp/bflTfob+MWi8Vi+cgDzZpONLUfx5kAAAAASUVORK5CYII=","orcid":"","institution":"Beijing Electronic Science and Technology Institute","correspondingAuthor":true,"prefix":"","firstName":"Furong","middleName":"","lastName":"Yu","suffix":""},{"id":447921763,"identity":"35a01e3d-caa5-464e-a39c-e23609b5e0ba","order_by":2,"name":"Lei Zhang","email":"","orcid":"","institution":"Beijing Electronic Science and Technology Institute","correspondingAuthor":false,"prefix":"","firstName":"Lei","middleName":"","lastName":"Zhang","suffix":""},{"id":447921764,"identity":"0b81628f-5553-4565-a52e-a70e8bfa858b","order_by":3,"name":"Jianxin Wang","email":"","orcid":"","institution":"Beijing Electronic Science and Technology Institute","correspondingAuthor":false,"prefix":"","firstName":"Jianxin","middleName":"","lastName":"Wang","suffix":""}],"badges":[],"createdAt":"2025-03-20 06:53:27","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-6266839/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6266839/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":83878732,"identity":"d4a312d3-dbcb-4479-8321-2deadb20dd34","added_by":"auto","created_at":"2025-06-04 04:23:36","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":407045,"visible":true,"origin":"","legend":"","description":"","filename":"AHardwareTrojanDetectionMethodBasedonRecognizingtheWaveformSimilarityofOscillatingCircuit.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6266839/v1_covered_dc94818b-6599-4170-a1e1-0a27ba734275.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"A Hardware Trojan Detection Method Based on Recognizing the Waveform Similarity of Oscillating Circuit","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"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":"Hardware Trojan detection, AES, Oscillation circuit, PCA, Correlation analysis","lastPublishedDoi":"10.21203/rs.3.rs-6266839/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6266839/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eTo solve the problem that the hardware Trojan occupies a small number of resources, and the triggering condition is difficult to detect, this paper proposes a Hardware Trojan detection method based on recognizing the waveform similarity of oscillation circuit. First, a model for detecting the hardware Trojan is built. Then, four hardware Trojans aimed at the AES encryption chips in the Trust-Hub library are detected respectively by using a ring oscillation circuit or a multivibrator circuit with external connection. The square wave signals at the feature points are extracted by principal component analysis and the results were obtained by correlation analysis, i.e. recognizing the waveform similarity by calculating the coefficient of association. Experimental results show that the four hardware Trojans could be identified, and one of the four Hardware Trojans only occupies 0.26% of the total chip resources. 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