Graph-Based Digital Twin Blueprint for Securing 6G Local Area Networks

The rise of sixth-generation (6G) wireless technologies is expected to transform local connectivity through ultra-low latency communication, autonomous network management, and massive device integration. Among these, 6G Local Area Networks (LANs) are emerging as key enablers for smart homes and industrial automation, offering decentralised and intelligent control at the network edge. However, it introduces critical security requirements. Traditional intrusion detection methods, based on static rules and signatures, are limited in addressing dynamic device interactions, strict latency constraints, and increasingly stealthy cyberattacks expected in 6G LANs. To overcome these limitations, this work introduces a graph-based security framework using digital twins (DTs) designed for 6G LANs. The proposed solution integrates three core components. First, a graph-based anomaly detection module models the real-time network structure using GraphSAGE and Graph Attention Networks (GAT) to enable efficient and context-aware threat identification. Second, an explainability module based on Shapley Additive Explanations (SHAP) provides transparent decision-making and introduces a confidence-aware mechanism to autonomously or collaboratively handle detected threats. Third, a self-healing module applies mitigation actions dynamically, enabling fast, adaptive, and explainable responses while allowing human oversight when needed. The effectiveness of the framework is validated through extensive experiments on a 6G LAN testbed and an external dataset. Results show that our solution reduces end-to-end detection latency by up to 63%, lowers mitigation reaction time by 55%, and decreases processing overhead by 20%, while improving detection accuracy by up to 25% compared to conventional methods. These results highlight the practical benefits of using DTs for real-time and explainable 6G LAN security. Our work offers a scalable and locally deployable solution that meets the performance and transparency needs of emerging autonomous networks.