Multifactor Trust-Driven Secure Communication Model for Cloud-Based Digital Twins

Abstract

Cloud-based digital twin (DT) platforms enable real-time monitoring, simulation, and collaborative decision-making across distributed clients. However, ensuring secure and trustworthy communication remains a critical challenge due to heterogeneous client behavior, resource contention, and evolving adversarial threats. This article proposes the multifactor trust-driven secure communication (MT-SeCom) framework to enforce resilient and intelligent collaboration in DT-enabled cloud environments. MT-SeCom operates through four coordinated phases: First, multifactor trust monitoring, capturing temporal, contextual, and federated trust signals; second, adaptive trust evaluation, adjusting trust weights based on network dynamics and threat intensity; third, Transformer-based trusted client classification, combining anomaly detection with supervised learning to accurately identify malicious or unreliable nodes; and finally, resilient communication management, optimizing routing, isolating compromised clients, and ensuring service continuity. A real-world testbed and comprehensive experiments demonstrate that MT-SeCom significantly enhances secure communication, mitigates cascading adversarial effects, and maintains high resilience under fluctuating attack conditions. MT-SeCom achieves an average 18.7% improvement in threat detection accuracy and a 24.3% reduction in anomaly occurrences compared to existing methods, confirming its robustness, scalability, and practical suitability for heterogeneous cloud-based DT ecosystems.