Encryption Traffic Classification Method Based on ConvNeXt and Bilinear Attention Mechanism

The rapid growth in internet traffic resulted to the emergence of network traffic categorization as a crucial area of research in network performance and management. This technological advancement has demonstrated its efficacy in aiding network administrators to identify anomalies within network behavior. However, the widespread adoption of encryption technology and the continual evolution of encryption protocols present a novel challenge in the classification of encrypted traffic. Addressing this challenge, this paper introduces an innovative methodology for classifying encrypted traffic by harnessing ConvNeXt and a fusion attention mechanism. Through the representation of traffic data as images and the integration of a bilinear attention mechanism into the model, our proposed approach attains heightened precision in the classification of encrypted network traffic. To substantiate the effectiveness of our methodology, experiments were conducted employing the publicly available ISCX VPN-nonVPN dataset. The experimental findings showcase superior recognition performance, underscoring the efficacy of the proposed approach.