Digital Twin Vulnerabilities in Industrial Cyber-Physical Systems: A Security Framework for Threat Simulation and Containment

Abstract

As part of Industry 4.0, digital twins (DTs) and industrial cyber-physical systems (CPS) have transformed predictive maintenance, real-time control, and operational efficiency. Data synchronization, communication interfaces, model logic, and deployment procedures are just a few areas where these integrations have shown serious flaws. This article presents a comprehensive security framework for DT-enabled CPS that can simulate and contain threats. It starts with classifying unique digital twin vulnerabilities and looking at the danger zone using situational modeling. For a more secure system, they offer a multi-tiered design with features like sandboxing, anomaly detection, model rollback, and quarantine. The architecture outperforms conventional CPS-only defenses in terms of detection accuracy, reaction time, and operational downtime in a smart factory case study. In order to safeguard the rapidly expanding DT-CPS ecosystem, this study shows that we need to look for integrated security solutions that combine monitoring, containment, and recovery.