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.

翻译：传统加密流量分类方法在开放世界假设下面临重大挑战，即同时分类已知应用并检测未知应用。为此，我们提出了一种新颖的开世界对比预训练框架。该框架通过对比预训练获得鲁棒的特征表示，并基于此确定球形映射空间以寻找每个已知类别的边缘流量，进而训练生成对抗网络合成与已知部分相似但不属于任何类别的新流量。这些合成流量被分配到Softmax的未知节点以修改分类器，从而有效增强对已知流量的敏感性并显著抑制未知流量。在三个数据集上的大量实验表明，OWCP显著优于现有加密流量分类及通用开世界分类方法。此外，我们进行了全面的消融研究和敏感性分析，以验证OWCP的每个核心组成部分。