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A Digital Twin to Improve Cyber-Social Impact

Vita Santa Barletta, Danilo Caivano, Gabriel Cellammare, Andrea Dimauro, Pasquale Mesagna, Massimiliano Morga · 2026 · arXiv (Cornell University)

DOI: 10.48550/arxiv.2610.28430 · cited by 0 works (OpenAlex) · open-access PDF

German

Die zunehmende Komplexität der gegenwärtigen Entwürfe von Industrial Control Systems (ICS) und ihre Integration mit IT-Frameworks hat die Cyber-Bedrohungslandschaft erheblich erweitert. Traditionelle Cyber-Sicherheitsmodelle, die hauptsächlich auf der Konzeption von Vertraulichkeit, Integrität und Verfügbarkeit (CIA-Triade) basieren, versagen, die breiteren gesellschaftlichen und operativen Auswirkungen von Cyber-Angriffen zu erfassen. Dieses Papier schlägt einen neuen Rahmen vor, der die Digital Twin-Technologie mit einem Cyber-Social Security-Modell integriert, um Cyber-Bedrohungen in kritischen Infrastrukturen zu simulieren, zu analysieren und zu quantifizieren. Virtualisierte ICS-Umgebungen, die mit Mininet

Machine translation (Meta M2M100); the English original follows.

Abstract

The increasing complexity of Industrial Control Systems (ICS) and their integration with IT infrastructures has significantly expanded the cyber threat landscape. Traditional cybersecurity models, primarily based on the Confidentiality, Integrity, and Availability (CIA triad), fail to capture the broader societal and operational impacts of cyberattacks. This paper proposes a novel framework that integrates Digital Twin technology with a Cyber-Social Security model to simulate, analyze, and quantify cyber threats in critical infrastructures. Virtualized ICS environments built with Mininet and MiniCPS are used to model attack scenarios and assess their consequences across technical and social dimensions. At the core of the framework lies the Cyber-Social Vulnerability Scoring System (CSVSS), a quantitative metric that extends CVSS with a Social Impact dimension grounded in the Entity and Place pillars, producing a single, reproducible severity score that reflects real-world consequences. Crucially, the framework does not automate risk assessment but keeps the human analyst at its center: a Large Language Model (LLM) performs a first-pass analysis, mapping vulnerabilities and drafting risk reports, while the analyst, supported by explainable AI feedback, validates the findings and retains decision authority over critical cases. Experimental results demonstrate the framework's ability to model and score cyber-social impact, while the human-in-the-loop design is presented as a conceptual principle to preserve oversight.

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Topics: Computer science, Vulnerability (computing), Dimension (graph theory), Metric (unit), Computer security, Risk analysis (engineering)

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