CyberTwine: A comprehensive cybersecurity resilience framework for digital twin systems in smart cities and critical infrastructure

Digital twin technology has emerged as a transformative paradigm for creating virtual replicas of physical assets, enabling real-time monitoring, predictive maintenance, and data-driven decision-making across diverse domains including smart cities, healthcare, energy systems, and industrial automation. However, the deep interconnection between physical and virtual layers inherent in digital twin architectures introduces significant cybersecurity vulnerabilities that can compromise operational integrity, data confidentiality, and system availability. This paper presents CyberTwine, a comprehensive cybersecurity resilience framework specifically designed for digital twin systems deployed in smart city and critical infrastructure environments. The framework integrates multi-layered defense mechanisms encompassing authentication, encryption, anomaly detection, and continuous monitoring across all layers of the digital twin stack. A systematic threat taxonomy is developed, classifying vulnerabilities across data, model, communication, physical, and application layers. Experimental evaluation demonstrates that CyberTwine achieves a threat detection accuracy of 94.6%, anomaly detection rate of 92.1%, and data integrity verification of 96.3%, representing substantial improvements over baseline approaches. The framework's modular architecture enables scalable deployment across heterogeneous digital twin environments while maintaining robust security postures. The findings contribute to advancing the security foundations necessary for reliable and trustworthy digital twin adoption in safety-critical applications.