Review and Perspective on Encrypted Traffic Classification Based on Contrastive Learning

Encrypted network traffic classification plays an important role in today's network security and performance optimization. Through the classification and identification of network traffic, it can better monitor and manage network traffic, optimize the allocation and utilization of network resources, identify and prevent all kinds of network attacks, and improve the security, stability and efficiency of network. With the continuous expansion of network scale, encrypted network traffic classification will become an important part of network security. Based on the related research work of encrypted traffic identification based on contrastive learning, this paper proposes a framework of encrypted traffic identification based on contrastive learning, reviews part of the research work through feature construction and model architecture, and analyzes the challenges faced by contrastive learning based encrypted traffic identification.