Shapelet and Graph Convolutional Network with transformer for Channel Access Attacks classification

Abstract

The open and broadcast nature of wireless communication makes signals susceptible to Channel Access Attacks (CAA) at Medium Access Control (MAC) layer, disrupting network performance. Existing Graph Neural Network (GNN)-based detection methods face critical limitations: graph construction from time series often disrupts temporal continuity, and detection accuracy degrades as the distance from attack source increases. To address these challenges, we propose a novel node-level classification framework, Shapelet and Transformer Graph Convolutional Network (SHA-TGCN). The key innovations lies in leveraging GNN inherent advantages: lower data requirements and lower computational complexity. SHA-TGCN requires less training data and has lower complexity than traditional neural networks, benefiting from local feature aggregationand our compact Shapelet-based structure, making it suitable for real-time CAA detection in resource-constrained wireless network environments. Experimental results demonstrate that SHA-TGCN outperforms other GNNs with an average classification accuracy of 80.31% and fast computational efficiency.