UAV network intrusion detection method based on spatio-temporal graph convolutional network

Compared with ground networks, UAV networks have the characteristics of fast moving nodes, frequent topology changes, and unreliable communication links. Traditional intrusion detection methods are difficult to apply. Aimed at the spatio-temporal dynamic characteristics of UAV networks, an intrusion detection method: Attention-based Spatio-Temporal Graph Convolutional Network (ATGCN) is proposed, which combines graph convolutional network and gated recursive unit into spatio-temporal graph convolutional network. The spatio-temporal graph convolutional network extracts the spatio-temporal evolution characteristics of the network from complex and changeable data, attention mechanism is used to extract the features most relevant to intrusion detection, and the support vector machine is used as the last layer of the model for classification to identify network attacks. The experimental analysis of multiple datasets shows that the proposed method can adapt to the dynamics and instability of UAV networks, has higher accuracy and lower false positive rate than traditional detection methods, and has good robustness and adaptability. © 2021, Editorial Board of JBUAA. All right reserved.