Multi-Factor Trust-Driven Secure Communication Model for Cloud-Based Digital Twins

2026-05-22

AI Summary

This work addresses the challenges of secure and trustworthy communication in cloud-based digital twins, which arise from client heterogeneity, resource contention, and dynamic threats. To tackle these issues, the authors propose MT-SeCom, a framework that integrates temporal, contextual, and federated trust signals to enable intelligent and resilient communication through a four-stage collaborative mechanism. This mechanism features multi-factor trust modeling, adaptive weight optimization, Transformer-driven malicious node identification, and resilient routing isolation. Experimental evaluation on a real-world platform demonstrates that MT-SeCom improves threat detection accuracy by an average of 18.7% and reduces anomalous events by 24.3%, significantly enhancing system robustness and scalability.

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 paper proposes the Multi-Factor 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: (i) Multi-Factor Trust Monitoring, capturing temporal, contextual, and federated trust signals; (ii) Adaptive Trust Evaluation, adjusting trust weights based on network dynamics and threat intensity; (iii) Transformer-Based Trusted Client Classification, combining anomaly detection with supervised learning to accurately identify malicious or unreliable nodes; and (iv) 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.

Problem

• Digital Twin
• Secure Communication
• Trust Management
• Cloud Computing
• Adversarial Threats

Innovation

• Multi-Factor Trust
• Transformer-Based Classification
• Secure Communication
• Digital Twin
• Resilient Cloud