标题： 用MH-Net革新加密流量分类：一种多视图异构图模型 显示英文标题

标题： Revolutionizing Encrypted Traffic Classification with MH-Net: A Multi-View Heterogeneous Graph Model

摘要： 随着网络安全的重要性日益增加，加密流量的分类已成为一项紧迫的挑战。 传统的基于字节的流量分析方法受到信息粒度的限制，无法充分利用字节之间的多样化相关性。 为解决这些限制，本文引入了MH-Net，这是一种新的网络流量分类方法，利用多视图异构流量图来建模流量字节之间的复杂关系。 MH-Net的核心在于将不同数量的流量位聚合为多种类型的流量单元，从而构建具有不同信息粒度的多视图流量图。 通过考虑不同类型的字节相关性，如头部-负载关系，MH-Net进一步赋予流量图异质性，显著提升了模型性能。 值得注意的是，我们采用多任务方式的对比学习来增强所学流量单元表示的鲁棒性。 在ISCX和CIC-IoT数据集上进行的包级和流级流量分类任务实验表明，与数十种最先进的方法相比，MH-Net实现了最佳的整体性能。 显示英文摘要

摘要： With the growing significance of network security, the classification of encrypted traffic has emerged as an urgent challenge. Traditional byte-based traffic analysis methods are constrained by the rigid granularity of information and fail to fully exploit the diverse correlations between bytes. To address these limitations, this paper introduces MH-Net, a novel approach for classifying network traffic that leverages multi-view heterogeneous traffic graphs to model the intricate relationships between traffic bytes. The essence of MH-Net lies in aggregating varying numbers of traffic bits into multiple types of traffic units, thereby constructing multi-view traffic graphs with diverse information granularities. By accounting for different types of byte correlations, such as header-payload relationships, MH-Net further endows the traffic graph with heterogeneity, significantly enhancing model performance. Notably, we employ contrastive learning in a multi-task manner to strengthen the robustness of the learned traffic unit representations. Experiments conducted on the ISCX and CIC-IoT datasets for both the packet-level and flow-level traffic classification tasks demonstrate that MH-Net achieves the best overall performance compared to dozens of SOTA methods.