A Metric Method for T/R Component Health Measurement for Cyber Defense Based on Sparse Preserving Projection and Hidden Markov  Model

A Metric Method for T/R Component Health Measurement for Cyber Defense Based on Sparse Preserving Projection and Hidden Markov Model | 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 A Metric Method for T/R Component Health Measurement for Cyber Defense Based on Sparse Preserving Projection and Hidden Markov Model Sheng Hong, Xuanqi Wang, Weiwei Jiang, Jingsong Zheng, Jiacheng Wang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9414249/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 6 You are reading this latest preprint version Abstract Cyber attacks on communication and radar systems increasingly exploit physical-layer vulnerabilities: targeted interference, power-surge injections, and coordinated cyber-physical intrusions can accelerate T/R module degradation, silently eroding beam-forming accuracy, jamming resistance, and encrypted-link integrity long before any network-layer anomaly is detected. Defending against such threats requires continuous health monitoring of T/R modules for effective cyber defense; however, existing data-driven methods fail to produce stable, monotone health indicators from multivariate RF sensor data, as operational load variations obscure genuine degradation trends and ground-truth failure labels are rarely available. We propose a framework combining Time-Smoothness Preserving Sparse Projection (TSP-SPP) with a Continuous Hidden Markov Model (CHMM) and Symmetric Kullback--Leibler divergence to construct a normalised Health-ability (HA) index in \(([0,1])\) . TSP-SPP encodes temporal continuity into the projection geometry, suppressing load-driven transients while preserving genuine degradation trends; Sym-KL captures both early covariance broadening and late-stage mean shifts that point-level metrics miss. The resulting HA serves as an early-warning signal, enabling operators to detect physical-layer compromise and initiate timely countermeasures before mission-critical capabilities are lost. Experiments on a full life-cycle T/R module dataset show a Monotonicity score of 0.542 and irregularity penalty of 0.032, outperforming PCA, LPP, and SPP baselines on both metrics. Health-ability Time-Smoothness Preserving Sparse Projection Hidden Markov Model Kullback--Leibler Divergence Cyber Defense Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 12 May, 2026 Reviewers agreed at journal 24 Apr, 2026 Reviewers invited by journal 24 Apr, 2026 Editor assigned by journal 19 Apr, 2026 Submission checks completed at journal 19 Apr, 2026 First submitted to journal 14 Apr, 2026 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-9414249","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":634119120,"identity":"581dabf8-8a19-420e-952d-3c42e35af77e","order_by":0,"name":"Sheng Hong","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA6UlEQVRIiWNgGAWjYDACCSBmbAAS7D1QkQNEa+E5Q7IWiRwitcjPbj728OsOmzz5yLfHJH7UMMjx3Uhg/FyAR4vBnWPpxrJn0ooNb+elSfYcYzCWvJHALD0DnxaJHDNpybbDiRtn55hJ8LAxJG64kcDGzIPPYTPyv0G0zDxjJvnnH0M9QS0MN3LYJD8CtcyX4DGT5m1jSDAgpMXgRpqZNGNbWuIGnrxka9k+CcOZZx42S+N3WPIzyZ9tNonz288evPnmm4083/Hkg5/xOgwIwM4wOABmw6MJP2D8AbKOsLpRMApGwSgYqQAAvlpN3TaC0/UAAAAASUVORK5CYII=","orcid":"","institution":"Beihang University","correspondingAuthor":true,"prefix":"","firstName":"Sheng","middleName":"","lastName":"Hong","suffix":""},{"id":634119121,"identity":"9198554b-fb15-4a8d-ac3f-18e25b5dd314","order_by":1,"name":"Xuanqi Wang","email":"","orcid":"","institution":"Nanchang University","correspondingAuthor":false,"prefix":"","firstName":"Xuanqi","middleName":"","lastName":"Wang","suffix":""},{"id":634119125,"identity":"64124227-fe4f-4f8b-9b8d-7da36589ab2d","order_by":2,"name":"Weiwei Jiang","email":"","orcid":"","institution":"Beijing University of Posts and Telecommunications","correspondingAuthor":false,"prefix":"","firstName":"Weiwei","middleName":"","lastName":"Jiang","suffix":""},{"id":634119128,"identity":"2ae62f95-6f25-4e44-b1fe-df628902281f","order_by":3,"name":"Jingsong Zheng","email":"","orcid":"","institution":"Nanchang University","correspondingAuthor":false,"prefix":"","firstName":"Jingsong","middleName":"","lastName":"Zheng","suffix":""},{"id":634119130,"identity":"a259bae2-a32c-4801-b586-624585a8e261","order_by":4,"name":"Jiacheng Wang","email":"","orcid":"","institution":"Beihang University","correspondingAuthor":false,"prefix":"","firstName":"Jiacheng","middleName":"","lastName":"Wang","suffix":""}],"badges":[],"createdAt":"2026-04-14 10:38:14","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9414249/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9414249/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":108494015,"identity":"39090451-ee5a-4369-8b30-d6e194c3c4b7","added_by":"auto","created_at":"2026-05-05 10:02:17","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1854584,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9414249/v1_covered_b050cc16-d2bb-42eb-b899-bd68ca6a3e31.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"A Metric Method for T/R Component Health Measurement for Cyber Defense Based on Sparse Preserving Projection and Hidden Markov Model","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"international-journal-of-machine-learning-and-cybernetics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jmlc","sideBox":"Learn more about [International Journal of Machine Learning and Cybernetics](http://actavetscand.biomedcentral.com/)","snPcode":"13042","submissionUrl":"https://submission.nature.com/new-submission/13042/3","title":"International Journal of Machine Learning and Cybernetics","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Health-ability, Time-Smoothness Preserving Sparse Projection, Hidden Markov Model, Kullback--Leibler Divergence, Cyber Defense","lastPublishedDoi":"10.21203/rs.3.rs-9414249/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9414249/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eCyber attacks on communication and radar systems increasingly exploit physical-layer vulnerabilities: targeted interference, power-surge injections, and coordinated cyber-physical intrusions can accelerate T/R module degradation, silently eroding beam-forming accuracy, jamming resistance, and encrypted-link integrity long before any network-layer anomaly is detected. Defending against such threats requires continuous health monitoring of T/R modules for effective cyber defense; however, existing data-driven methods fail to produce stable, monotone health indicators from multivariate RF sensor data, as operational load variations obscure genuine degradation trends and ground-truth failure labels are rarely available. We propose a framework combining Time-Smoothness Preserving Sparse Projection (TSP-SPP) with a Continuous Hidden Markov Model (CHMM) and Symmetric Kullback--Leibler divergence to construct a normalised Health-ability (HA) index in \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(([0,1])\\)\u003c/span\u003e\u003c/span\u003e. TSP-SPP encodes temporal continuity into the projection geometry, suppressing load-driven transients while preserving genuine degradation trends; Sym-KL captures both early covariance broadening and late-stage mean shifts that point-level metrics miss. The resulting HA serves as an early-warning signal, enabling operators to detect physical-layer compromise and initiate timely countermeasures before mission-critical capabilities are lost. Experiments on a full life-cycle T/R module dataset show a Monotonicity score of 0.542 and irregularity penalty of 0.032, outperforming PCA, LPP, and SPP baselines on both metrics.\u003c/p\u003e","manuscriptTitle":"A Metric Method for T/R Component Health Measurement for Cyber Defense Based on Sparse Preserving Projection and Hidden Markov Model","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-05-05 07:55:17","doi":"10.21203/rs.3.rs-9414249/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"56780498159669000040255833038784830806","date":"2026-05-12T05:00:28+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"12013231423230471744042415108469202828","date":"2026-04-24T22:31:30+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-24T11:53:15+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-19T08:14:39+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-04-19T04:37:09+00:00","index":"","fulltext":""},{"type":"submitted","content":"International Journal of Machine Learning and Cybernetics","date":"2026-04-14T10:24:08+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"international-journal-of-machine-learning-and-cybernetics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jmlc","sideBox":"Learn more about [International Journal of Machine Learning and Cybernetics](http://actavetscand.biomedcentral.com/)","snPcode":"13042","submissionUrl":"https://submission.nature.com/new-submission/13042/3","title":"International Journal of Machine Learning and Cybernetics","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"90e5ea58-dee8-46af-adb0-7e027f60e34a","owner":[],"postedDate":"May 5th, 2026","published":true,"recentEditorialEvents":[{"type":"reviewerAgreed","content":"56780498159669000040255833038784830806","date":"2026-05-12T05:00:28+00:00","index":16,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-05T07:55:18+00:00","versionOfRecord":[],"versionCreatedAt":"2026-05-05 07:55:17","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9414249","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9414249","identity":"rs-9414249","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}