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.

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