A Multi-View Deep Learning Model for Encrypted Website Service Classification

Kaiqi Liang, Gaopeng Gou, Cuicui Kang, Chang Liu, Min Yang, Yu Guo

Abstract: This paper presents a multi-view deep learning model for encrypted traffic classification which uses different neural network structures to process different types of features. Compared to previous work, our multi-view deep learning model can make full use of different types of features extracted from the flow. We evaluate our deep learning model in the real- world datasets acquired from the website servers of a large internet corporation. We found that the proposed multi-view deep learning model gets better performance than the state-of-the-art. Moreover, this paper focuses on a new traffic classification scenario in which the traffic under the specific website is classified on the basis of different website services. Each website service represents a specific website function which can be composed of several webpages. Therefore, the granularity of website service classification is between website classification and webpage classification.