Increased awareness of privacy protection has led to a surge in the volume of encrypted traffic, which creates a heavy burden for efficient network management (e.g. quality-of-service guarantees). The opacity of encrypted traffic essentially requires high computational overheads to make traffic classification, which is even worse when encrypted traffic surges. However, existing deep learning approaches sacrifice the efficiency to obtain high-precision classification results, which are no longer suitable for scenarios with large volumes of encrypted traffic. In this paper, a lightweight and online approach implemented as MATEC is proposed. The way we optimize the classification process follows the “Maximizing the reuse of thin modules” design principle. The multi-head attention and the convolutional network are adopted in the thin module. Attributed to the one-step interaction of all packets and the parallel computing of the multi-head attention mechanism, a key advantage of our model is that the number of parameters and running time are significantly reduced. In addition, the effectiveness and efficiency of convolutional networks have been proved in traffic classification. Comparisons to the existing state-of-the-art models on three typical datasets demonstrate that the proposed MATEC model has higher accuracy and running efficiency. In addition, the number of parameters is reduced to 1.8% of the state-of-the-art models and the training time halves.

隐私保护意识的增强导致加密流量的激增，这给高效的网络管理（例如服务质量保证）带来了沉重的负担。加密流量的不透明本质上需要很高的计算开销来进行流量分类，当加密流量激增时，这种情况更糟。然而，现有的深度学习方法为了获得高精度的分类结果牺牲了效率，不再适用于加密流量大的场景。在本文中，提出了一种作为 MATEC 实现的轻量级和在线方法。我们优化分类过程的方式遵循“最大化薄模块的重用”设计原则。瘦模块中采用了多头注意力和卷积网络。由于所有数据包的一步交互和多头注意力机制的并行计算，我们模型的一个关键优势是参数数量和运行时间显着减少。此外，卷积网络的有效性和效率在流量分类中得到了证明。在三个典型数据集上与现有最先进模型的比较表明，所提出的 MATEC 模型具有更高的准确性和运行效率。此外，参数数量减少到最先进模型的 1.8%，训练时间减半。卷积网络的有效性和效率已经在流量分类中得到证明。在三个典型数据集上与现有最先进模型的比较表明，所提出的 MATEC 模型具有更高的准确性和运行效率。此外，参数数量减少到最先进模型的 1.8%，训练时间减半。卷积网络的有效性和效率已经在流量分类中得到证明。在三个典型数据集上与现有最先进模型的比较表明，所提出的 MATEC 模型具有更高的准确性和运行效率。此外，参数数量减少到最先进模型的 1.8%，训练时间减半。