Blockchain-enabled 6G wireless network securities using multi-relational graph attention disentangled cascaded graph convolution networks

Abstract

The emergence of sixth-generation (6 G) wireless networks introduces transformative capabilities such as ultra-low latency, massive device connectivity, and real-time intelligent services. However, the inherently distributed and heterogeneous architecture of 6 G significantly escalates vulnerabilities to cyber threats, unauthorized access, and data breaches across large-scale decentralized infrastructures. Existing security mechanisms are inadequate in modeling complex multi-relational interactions among network entities and often fail to ensure trust, transparency, and tamper-resistance at scale. To address these challenges, this research proposes a novel Blockchain-Enabled 6 G Wireless Network Security framework integrating Multi-Relational Graph Attention and Disentangled Cascaded Graph Convolution Network (Multi-RACG) model. This hybrid graph-based model captures high-order relational dependencies while disentangling semantic features across graph channels to enable precise, context-aware intrusion detection. A Dandelion Optimization Algorithm (DOA) is employed to fine-tune model parameters and optimize network architecture, ensuring rapid convergence and reduced computational overhead. Additionally, a Proof-of-Work-Based Weighted Mining (PoWBWM) consensus protocol strengthens blockchain operations by incorporating dynamic trust metrics, enhancing data integrity and resilience against malicious manipulation. Experimental results demonstrate the framework's superiority, achieving 99.9 % detection accuracy with minimal false positives and computational loss, positioning it as a highly scalable and intelligent security solution for future 6 G ecosystems.