首页|End-to-end encrypted network traffic classification method based on deep learning

End-to-end encrypted network traffic classification method based on deep learning

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Network traffic classification,which matches network traffic for a specific class of different granularities,plays a vital role in the domain of network administration and cyber security.With the rapid development of network communication techniques,more and more network applications adopt encryption techniques during communication,which brings significant challenges to traditional network traffic classification methods.On the one hand,traditional methods mainly depend on matching features on the application layer of the ISO/OSI reference model,which leads to the failure of classifying encrypted traffic.On the other hand,machine learning-based methods require human-made features from network traffic data by human experts,which renders it difficult for them to deal with complex network protocols.In this paper,the convolution attention network (CAT) is proposed to overcom those difficulties.As an end-to-end model,CAT takes raw data as input and returns classification results automatically,with engineering by human experts.In CAT,firstly,the importance of different bytes with an attention mechanism of network traffic is achieved.Then,convolution neural network (CNN) is used to learn features automatically and feed the output into a softmax function to get classification results.It enables CAT to learn enough information from network traffic data and ensure the classified accuracy.Extensive experiments on the public encrypted network traffic dataset ISCX2016 demonstrate the effectiveness of the proposed model.

network traffic classificationconvolution neural networkattention mechanismnetwork managementcyber security

Tian Shiming、Gong Feixiang、Mo Shuang、Li Meng、Wu Wenrui、Xiao Ding

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Department of Power Consumption and Energy Efficiency, China Electric Power Research Institute Co.Ltd., Beijing 100192, China

School of Computer Science, Beijing University of Posts and Telecommunications, Beijing 100876, China

State Grid Corporation of China, Beijing 100031, China

This work was supported by the State Grid Science and Technology Project Research on Key Technologies and Applications of Self-S

5442YD180015.

2020

中国邮电高校学报(英文版)
北京邮电大学

中国邮电高校学报(英文版)

CSCDEI
影响因子：0.419
ISSN：1005-8885
年,卷(期)：2020.27(3)
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