Digital Twin Security: Attack Surface Analysis and Mitigation in Smart Infrastructure

Abstract: This study investigates the cybersecurity vulnerabilities associated with digital twins in smart infrastructure and proposes a comprehensive framework for attack surface analysis and mitigation. As digital twin systems become integral to real-time monitoring, predictive maintenance, and optimization in smart cities and industrial applications, their complexity and interconnectivity introduce significant attack surfaces. This research employs a layered architecture model to identify potential vulnerabilities across physical, network, data, and application layers. Threat modeling, combined with simulation-based validation, reveals that attack vectors like data poisoning, unauthorized access, and sensor spoofing present critical risks to digital twin operations. Regression and predictive analyses using datasets from smart building simulations underscore the correlation between system complexity and vulnerability frequency. The findings support the development of a risk-prioritized mitigation strategy involving encryption, anomaly detection, and access control policies. The proposed framework contributes to the secure deployment of digital twins in critical infrastructure. Keywords Digital Twin, Smart Infrastructure, Cybersecurity, Attack Surface, Threat Modeling, Mitigation Strategy