GAT-CNN Method for Encrypted Malicious Traffic Detection and Classification

This paper proposes a Graph Attention based Convolutional Neural Network (GAT-CNN) for encrypted malicious traffic classification and detection, which improves the performance of network encrypted malicious traffic detection, recognition, and classification. By establishing a connectivity diagram between nodes, the spatiotemporal features in network traffic records are strengthened, providing a feature basis for intrusion detection. Using graph convolutional neural networks to dynamically aggregate and extract node behaviors in the graph, generate feature representations with category discrimination ability, and allocate different weights through multi head attention mechanism to achieve accurate classification of network intrusion behavior. To verify the effectiveness of the method, experiments were conducted on the ISCX-VPN encrypted traffic dataset, with a detection accuracy of 98.79% and a recall rate of 99.24%, demonstrating that the proposed method exhibits good performance in detecting malicious traffic.