A Digital Twin-Integrated Framework for Dual Insider and External Cyber Threat Detection in Critical Infrastructure

A Digital Twin-Integrated Framework for Dual Insider and External Cyber Threat Detection in Critical Infrastructure | 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 Digital Twin-Integrated Framework for Dual Insider and External Cyber Threat Detection in Critical Infrastructure Sayed Athar Ali Hashmi, Pushpendra Tiwari This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8860486/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 The convergence of Information Technology (IT) and Operational Technology (OT) has significantly increased the cyber exposure of critical infrastructure systems such as power grids, healthcare platforms, transportation networks, and industrial control environments. These systems face dual cybersecurity risks from external adversaries, including ransomware and advanced persistent threats, as well as insider threats originating from authorized or compromised users whose actions often resemble legitimate behavior. This paper presents a Digital Twin-integrated cybersecurity framework for the simultaneous detection of insider and external cyber threats in heterogeneous critical infrastructure environments. The proposed framework maintains a continuously synchronized virtual replica of the operational system and integrates behavioural profiling, sequence-aware network traffic analysis, and contextual anomaly validation within a five-layer closed-loop architecture. A hybrid ensemble detection approach combining Isolation Forest, One-Class Support Vector Machine, Random Forest, and LSTM-based sequence modelling is employed to enhance detection accuracy while reducing false positives. Experimental evaluation using the CIC-IDS2018 dataset for external attacks and the CERT Insider Threat dataset demonstrates detection accuracies of 95.2% for insider threats and 97.1% for external attacks, with an average detection latency of 38 ms and a false positive rate below 2%. The results indicate that Digital Twin-based contextual modelling can significantly improve the precision and responsiveness of intrusion detection mechanisms in IT/OT-converged critical infrastructure systems. Digital twin security Insider threat detection Cyber-physical systems Intrusion detection systems Critical infrastructure protection Network anomaly detection 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. 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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-8860486","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":594003007,"identity":"2d950e55-1532-4c37-9cae-df3289fcddfc","order_by":0,"name":"Sayed Athar Ali Hashmi","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA90lEQVRIiWNgGAWjYBACAwYGNjDJIAHEHxtAYoyNB4jSwgPUwjizAaSTsYEILQwQLcy8YC0MDHi1mLMff/boRgGDvL1087PPtjts6nTbDwNtqbGJxqXFsifH3DjHgMGwR+aY8ezcM2kSZmcSgVqOpeU24HLYgRw2aaAWxh6JBGPm3LbDEmYHgFoYGw7j1nL++TOQFvseifTPzJYgLecfEtByI8EMpCWxRyLHmJkRpOUGIVtuvAFrSe65kVPM2NuWJrntBtCWBHx+OZ8OdNgfBtv2GembGX622fCbnU9/+OBDjQ1OLVDwH42fgF/5KBgFo2AUjAICAAAsHFy5Xve3QwAAAABJRU5ErkJggg==","orcid":"","institution":"Department of Higher Education","correspondingAuthor":true,"prefix":"","firstName":"Sayed","middleName":"Athar Ali","lastName":"Hashmi","suffix":""},{"id":594003008,"identity":"be48b5a0-8ffe-42c5-b0d4-549a136fbe31","order_by":1,"name":"Pushpendra Tiwari","email":"","orcid":"","institution":"Department of Higher Education","correspondingAuthor":false,"prefix":"","firstName":"Pushpendra","middleName":"","lastName":"Tiwari","suffix":""}],"badges":[],"createdAt":"2026-02-12 09:53:40","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8860486/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8860486/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[],"financialInterests":"No competing interests reported.","formattedTitle":"A Digital Twin-Integrated Framework for Dual Insider and External Cyber Threat Detection in Critical Infrastructure","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"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":"Digital twin security, Insider threat detection, Cyber-physical systems, Intrusion detection systems, Critical infrastructure protection, Network anomaly detection","lastPublishedDoi":"10.21203/rs.3.rs-8860486/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8860486/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe convergence of Information Technology (IT) and Operational Technology (OT) has significantly increased the cyber exposure of critical infrastructure systems such as power grids, healthcare platforms, transportation networks, and industrial control environments. These systems face dual cybersecurity risks from external adversaries, including ransomware and advanced persistent threats, as well as insider threats originating from authorized or compromised users whose actions often resemble legitimate behavior.\u003c/p\u003e \u003cp\u003eThis paper presents a Digital Twin-integrated cybersecurity framework for the simultaneous detection of insider and external cyber threats in heterogeneous critical infrastructure environments. The proposed framework maintains a continuously synchronized virtual replica of the operational system and integrates behavioural profiling, sequence-aware network traffic analysis, and contextual anomaly validation within a five-layer closed-loop architecture. A hybrid ensemble detection approach combining Isolation Forest, One-Class Support Vector Machine, Random Forest, and LSTM-based sequence modelling is employed to enhance detection accuracy while reducing false positives.\u003c/p\u003e \u003cp\u003eExperimental evaluation using the CIC-IDS2018 dataset for external attacks and the CERT Insider Threat dataset demonstrates detection accuracies of 95.2% for insider threats and 97.1% for external attacks, with an average detection latency of 38 ms and a false positive rate below 2%. 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