FlowCLIP: Contrastive Pretraining Using Domain Names for Encrypted Traffic Classification

Abstract

Network traffic classification enables website fingerprinting, intrusion detection, and quality of service management. However, developing methods that capture stable and generalizable traffic patterns remains challenging. We introduce FlowCLIP, a contrastive pretraining framework for learning traffic representations using only side-channel features: packet inter-arrival times, packet sizes, and packet directions. FlowCLIP feeds traffic flow features into a traffic encoder and the corresponding domain names into a text encoder, aligning their learned representations through a CLIP-style contrastive loss. After pretraining on the CESNET-QUIC22 dataset, we freeze the traffic encoder and evaluate it through linear probing. We also assess whether the pretrained representations transfer to a separate dataset, the UC Davis dataset. Through these evaluations, we show that domain names can be used directly for traffic representation learning without defining traffic classes.