With the increasing complexity of network protocol traffic in the modern network environment, the task of traffic classification is facing significant challenges. Existing methods lack research on the characteristics of traffic byte data and suffer from insufficient model generalization, leading to decreased classification accuracy. In response, we propose a method for encrypted traffic classification based on a Staggered Attention Network using Graph Neural Networks (SAT-Net), which takes into consideration both computer network topology and user interaction processes. Firstly, we design a Packet Byte Graph (PBG) to efficiently capture the byte features of flow and their relationships, thereby transforming the encrypted traffic classification problem into a graph classification problem. Secondly, we meticulously construct a GNN-based PBG learner, where the feature remapping layer and staggered attention layer are respectively used for feature propagation and fusion, enhancing the robustness of the model. Experiments on multiple different types of encrypted traffic datasets demonstrate that SAT-Net outperforms various advanced methods in identifying VPN traffic, Tor traffic, and malicious traffic, showing strong generalization capability.

中文翻译：

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SAT-Net：使用图神经网络进行加密流量分类的交错注意力网络


随着现代网络环境中网络协议流量的复杂性日益增加，流量分类的任务面临着重大挑战。现有方法缺乏对流量字节数据特征的研究，模型泛化不足，导致分类精度下降。作为回应，我们提出了一种基于使用图神经网络 （SAT-Net） 的交错注意力网络的加密流量分类方法，该方法同时考虑了计算机网络拓扑和用户交互过程。首先，我们设计了一个数据包字节图 （PBG） 来有效地捕获流的字节特征及其关系，从而将加密的流量分类问题转化为图分类问题;其次，我们精心构建了一个基于 GNN 的 PBG 学习器，其中特征重映射层和交错注意力层分别用于特征传播和融合，增强了模型的鲁棒性。在多种不同类型的加密流量数据集上的实验表明，SAT-Net 在识别 VPN 流量、Tor 流量和恶意流量方面优于各种高级方法，表现出很强的泛化能力。