With the rapid growth of the encrypted network traffic, the identification to it becomes a hot topic in information security. Since the existing methods have difficulties in identifying the application which the encrypted traffic belongs to, a new encrypted traffic identification scheme is proposed in this paper. The proposed scheme has two levels. In the first level, the entropy and estimation of Monte Carlo π value as features are used to identify the encrypted traffic by C4.5 decision tree. In the second level, the application types are distinguished from the encrypted traffic selected above. First, the variational automatic encoder is used to extract the layer features, which is combined with the frequently-used stream features. Meanwhile, the mutual information is used to reduce the dimensionality of the combination features. Finally, the random forest classifier is used to obtain the optimal result. Compared with the existing methods, the experimental results show that the proposed scheme not only has faster convergence speed but also achieves better performance in the recognition accuracy, recall rate, and F1-Measure, which is higher than 97%.

中文翻译：

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基于多层结构和变分自动编码器的加密交通识别方案

随着加密网络流量的快速增长，对其进行识别成为信息安全中的热门话题。由于现有方法难以识别加密流量所属的应用，因此提出了一种新的加密流量识别方案。提议的方案有两个层次。在第一层，蒙特卡罗π的熵和估计值作为特征用于通过C4.5决策树识别加密的流量。在第二级中，将应用程序类型与上面选择的加密流量区分开。首先，使用变分自动编码器提取图层特征，并将其与常用的流特征结合在一起。同时，互信息被用来减小组合特征的维数。最后，使用随机森林分类器获得最佳结果。与现有方法相比，实验结果表明，该方案不仅收敛速度更快，而且在识别精度，召回率和F1-Measure方面都达到了97％以上的较好性能。