  Traditional Encrypted Traffic Classification (ETC) methods face a significant
challenge in classifying large volumes of encrypted traffic in the open-world
assumption, i.e., simultaneously classifying the known applications and
detecting unknown applications. We propose a novel Open-World Contrastive
Pre-training (OWCP) framework for this. OWCP performs contrastive pre-training
to obtain a robust feature representation. Based on this, we determine the
spherical mapping space to find the marginal flows for each known class, which
are used to train GANs to synthesize new flows similar to the known parts but
do not belong to any class. These synthetic flows are assigned to Softmax's
unknown node to modify the classifier, effectively enhancing sensitivity
towards known flows and significantly suppressing unknown ones. Extensive
experiments on three datasets show that OWCP significantly outperforms existing
ETC and generic open-world classification methods. Furthermore, we conduct
comprehensive ablation studies and sensitivity analyses to validate each
integral component of OWCP.
