Detection Methods of Hardware Trojans Based on DBN and QPSO-OCSVM Anomalous Behavior Analysis

Detection Methods of Hardware Trojans Based on DBN and QPSO-OCSVM Anomalous Behavior Analysis | 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 Detection Methods of Hardware Trojans Based on DBN and QPSO-OCSVM Anomalous Behavior Analysis Lei ZHANG, Tian-Yu ZHANG, Chao-En XIAO, Jian-Xin WANG, Dongyang LIU, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4067325/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 At present, hardware trojan detection methods based on the side channel analysis technology mainly depend on the presence of the template chip, resulting in a problem that it is difficult to put the methods into practical applications. In addition, detection focus is always paid on the impact of the hardware trojan on the target circuit, and impact of hardware trojan trigger state on the target circuit is ignored. To address the abovementioned problems, a hardware trojan detection method based on anomalous behavior analysis is proposed in this paper. Firstly, detection theory based on anomalous behavior analysis is proposed, and an anomalous behavior analysis methods based on the DBN (Deep Belief Networks) and the QPSO-OCSVM (Quantum Particle Swarm Optimization-One Class Support Vector Machine) is designed, based on which a detection model is established. This paper first proposed the detection theory based on anomalous behavior analysis, designs the anomalous behavior analysis methods based on DBN and QPSO-OCSVM, and build a detection model on this basis. Then, a hardware trojan detection platform designed and implemented, where three trigger-type hardware Trojans for the AES(Advanced Encryption Standard) encryption in the international Trust-Hub library are selected as the detection object and implanted to the encryption chip. Finally, the DBN model is utilized to extract the feature and reduce the dimensionality of the captured behavior data. The QPSO-OCSVM is used for detecting the anomalous behavior and obtaining the final detection result. Experimental results show that the detection method proposed in this paper could achieve the detection accuracy higher than 99% for the hardware trojans with resource occupancy rates of only 0.33%, 0.34%, and 0.76%. Optimal analysis model is established and a performance comparison with the anomalous detection model based on Isolation Forest and Local Outlier Factor is implemented, exhibiting that the detection model proposed in this paper has the highest performance. hardware trojan detection anomalous behavior analysis deep belief networks quantum-behaved particle swarm optimization one class support vector machines 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. 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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-4067325","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":283177522,"identity":"9246d68c-ad02-48bb-a3da-5e4c4109c826","order_by":0,"name":"Lei ZHANG","email":"","orcid":"","institution":"Beijing Electronic Science and Technology Institute","correspondingAuthor":false,"prefix":"","firstName":"Lei","middleName":"","lastName":"ZHANG","suffix":""},{"id":283177523,"identity":"6dc08076-2971-4063-b8b5-e17a4b6abf2a","order_by":1,"name":"Tian-Yu 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Based on DBN and QPSO-OCSVM Anomalous Behavior Analysis","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":false,"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, anomalous behavior analysis, deep belief networks, quantum-behaved particle swarm optimization, one class support vector machines","lastPublishedDoi":"10.21203/rs.3.rs-4067325/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4067325/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAt present, hardware trojan detection methods based on the side channel analysis technology mainly depend on the presence of the template chip, resulting in a problem that it is difficult to put the methods into practical applications. In addition, detection focus is always paid on the impact of the hardware trojan on the target circuit, and impact of hardware trojan trigger state on the target circuit is ignored. To address the abovementioned problems, a hardware trojan detection method based on anomalous behavior analysis is proposed in this paper. Firstly, detection theory based on anomalous behavior analysis is proposed, and an anomalous behavior analysis methods based on the DBN (Deep Belief Networks) and the QPSO-OCSVM (Quantum Particle Swarm Optimization-One Class Support Vector Machine) is designed, based on which a detection model is established.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis paper first proposed the detection theory based on anomalous behavior analysis, designs the anomalous behavior analysis methods based on DBN and QPSO-OCSVM, and build a detection model on this basis. Then, a hardware trojan detection platform designed and implemented, where three trigger-type hardware Trojans for the AES(Advanced Encryption Standard) encryption in the international Trust-Hub library are selected as the detection object and implanted to the encryption chip. Finally, the DBN model is utilized to extract the feature and reduce the dimensionality of the captured behavior data. The QPSO-OCSVM is used for detecting the anomalous behavior and obtaining the final detection result. Experimental results show that the detection method proposed in this paper could achieve the detection accuracy higher than 99% for the hardware trojans with resource occupancy rates of only 0.33%, 0.34%, and 0.76%. Optimal analysis model is established and a performance comparison with the anomalous detection model based on Isolation Forest and Local Outlier Factor is implemented, exhibiting that the detection model proposed in this paper has the highest performance.\u003c/p\u003e","manuscriptTitle":"Detection Methods of Hardware Trojans Based on DBN and QPSO-OCSVM Anomalous Behavior Analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-03-27 20:25:35","doi":"10.21203/rs.3.rs-4067325/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","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}}],"origin":"","ownerIdentity":"9da8a2c9-72a6-4008-b89b-884a3f0576e6","owner":[],"postedDate":"March 27th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-07-02T06:21:28+00:00","versionOfRecord":[],"versionCreatedAt":"2024-03-27 20:25:35","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4067325","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4067325","identity":"rs-4067325","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}