基于自编码器的网络游戏流量分类

加密和动态端口技术使传统的流量分类技术不能满足网络游戏识别的性能需求, 本文提出了一种基于自编码器降维的端到端流量分类模型, 实现网络游戏流量的准确识别. 首先将原始流量预处理成784 B的一维会话流向量, 利用编码器进行无监督降维, 去除无效特征; 接着探索构建卷积神经网络与LSTM网络并联算法, 对降维后的样本进行空间和时序特征的提取和融合, 最后利用融合特征进行分类. 在自建的游戏流量数据集和公开数据集上测试, 本文模型在网络游戏流量识别方面达到了97.68%的准确率; 与传统端到端的网络流量分类模型相比, 本文所设计的模型更加轻量化, 具有实用性, 并且能够在资源有限的设备中方便部署.

Online Game Traffic Classification Based on Autoencoder

Encryption and dynamic port technology make the traditional traffic classification technology fail to meet the performance requirements of online game identification. In this study, an end-to-end traffic classification model based on auto-encoder dimension reduction is proposed to accurately identify online game traffic. First, the original traffic is preprocessed into a one-dimensional session flow quantity of 784 B, and the encoder is used for unsupervised dimension reduction and removing invalid features. Then, the parallel algorithm of the convolutional neural network and LSTM network is explored and constructed to extract and fuse spatial and temporal features of samples after dimension reduction. Finally, the fusion features are used for classification. When tested on the self-built game traffic dataset and the open dataset, the proposed model achieves an accuracy rate of 97.68% in online game traffic identification. Compared with the traditional end-to-end network traffic classification model, the model designed in this study is more lightweight and practical and can be easily deployed on devices with limited resources.