Simulation-Powered Cybersecurity: Real-Time Risk Assessment via Non-Intrusive Security Twin

Simulation-Powered Cybersecurity: Real-Time Risk Assessment via Non-Intrusive Security Twin | 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 Simulation-Powered Cybersecurity: Real-Time Risk Assessment via Non-Intrusive Security Twin Fabrizio Baiardi, Vincenzo Sammartino This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8418607/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract Digital twin technology is emerging as the cornerstone of next-generation cybersecurity. A security twin is a graph-based model which acts as a dynamic inventory enriched with vulnerability intelligence and that can mirror complex ICT infrastructures to predict the behavior of threats without disrupting live production environments. However, maintaining high-fidelity synchronisation between the infrastructure and the twin remains a challenge, mainly when active scanning cannot be employed. This paper introduces NotLine , a non-intrusive and fully automated platform that builds and updates a security twin through the continuous passive ingestion of multi-protocol network telemetry. NotLine leverages a distributed monitoring pipeline architecture to filter, normalize, and correlate heterogeneous traffic metadata in real-time. NotLine maps these data to the security twin. The core innovation of NotLine lies in its integration of this live model with an AI-driven Monte Carlo simulation engine . The engine uses the security twin to generate the state transitions of a threat actor as determined by the access rights and information it has acquired. This enables the quantification of risk exposure probabilistically and enables prescriptive analytics and preemptive remediation. We present a comprehensive evaluation of NotLine in a production environment and show that a hypoexponential mathematical model characterises the platform discovery pattern. According to this model, the platform maps the majority of assets within 48 hours, but a long-tail monitoring period is critical to capture all infrastructure components. These results confirm that NotLine provides a robust foundation for simulation-powered cybersecurity, bridging the gap between passive observation and proactive risk prediction. Digital Twin Cybersecurity Large Scale Distributed Simulation Passive Network Monitoring Monte Carlo Methods High-Performance Computing Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 03 Mar, 2026 Reviews received at journal 19 Feb, 2026 Reviewers agreed at journal 19 Feb, 2026 Reviews received at journal 08 Feb, 2026 Reviewers agreed at journal 04 Feb, 2026 Reviewers agreed at journal 16 Jan, 2026 Reviewers invited by journal 14 Jan, 2026 Editor assigned by journal 12 Jan, 2026 Submission checks completed at journal 23 Dec, 2025 First submitted to journal 21 Dec, 2025 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-8418607","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":575731849,"identity":"8a936dea-7533-4e23-a126-a7e80fbf26ff","order_by":0,"name":"Fabrizio Baiardi","email":"","orcid":"","institution":"University of Pisa","correspondingAuthor":false,"prefix":"","firstName":"Fabrizio","middleName":"","lastName":"Baiardi","suffix":""},{"id":575731851,"identity":"ee28dbb7-3775-4daf-84e4-1e341d4d1c5b","order_by":1,"name":"Vincenzo 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