Cybersecurity for Digital Twins
DOI:
https://doi.org/10.15662/IJEETR.2026.0804001Keywords:
Digital Twin, Cybersecurity, Anomaly Detection, Industry 5.0, Industrial IoT, Federated Learning, Cyber-Physical SystemsAbstract
Digital twins (DTs), a technology of Industry 4.0 and 5.0, are now playing a pivotal role in the real-time virtual representation of physical assets in manufacturing, aerospace, healthcare, and smart cities. The close coupling of the cyber and physical domains, however, creates a large and novel attack surface that is not well covered by traditional security frameworks. The Secure-AI Twin (SAT) framework is introduced in this paper as an all-encompassing cybersecurity concept for AI-powered digital twins covering four key areas: Vulnerability analysis throughout DT ecosystems, Anomaly detection using AI, Industrial IoT and Industry 5.0 security, and Real-time cyber-physical threat mitigation. The proposed multi-layered defense architecture has been implemented using advances in federated learning, explainable AI, and neural-network-based threat detection, resulting in a 27.4% increase in threat detection accuracy and a 21.2% decrease in latency. The framework tackles the challenges of fidelity, real-time synchronization, and security, known as the "Digital Twin Trilemma," with an integrated solution that includes blockchain-supported federated learning, autoencoder-based anomaly detection, and dynamic incident response. The framework has been proven effective against sophisticated cyber-physical attacks in both industrial control systems and IIoT environments through experimental validation
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