基于机器学习和深度学习的网络流量分类研究

doi:10.11959/j.issn.1000-0801.2021052

Abstract

Abstract:

With the continuous development of Internet technology and the continuous expansion of network scale, there are many different types of applications , and various new applications have endlessly emerged.In order to ensure the quality of service (QoS) and ensure network security, accurate and fast traffic classification is an urgent problem for both operators and network managers.Firstly, the problem definition and performance metrics of network traffic classification were given.Then, the traffic classification methods based on machine learning and deep learning were introduced respectively, the advantages and disadvantages of these methods were analyzed, and the existing problems were expounded.Next, the related work by focusing on the three problems encountered elaborated and analyzed in traffic classification when considering online deployment: dataset, zero-day application identification and the cost of online deployment, and further discusses the challenges faced by the current network traffic classification researches.Finally, the next research direction of network traffic classification was prospected.

Key words: QoS, network security, traffic classification, data collection, zero-day application identification

CLC Number:

TP393

Yue GU, Dan LI, Kaihui GAO. Research on network traffic classification based on machine learning and deep learning[J]. Telecommunications Science, 2021, 37(3): 105-113.

Figures/Tables 5

References 27

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	是否需要人为特征提取	是否适用于加密流量	是否能够满足实时性	准确性
基于机器学习方法	是	是	否	较高
基于数据包原始字节特征的深度学习方法	否	若仅基于包负载字节内容分类则不适用；若仅基于包头字节内容则适用	是	较高
基于流内数据包序列特征的深度学习方法	否	是	否	极高

对比项	准确率	精度	召回率	F1分数
ACGAN	0.926 3	0.951 6	0.916 7	0.933 8
RBRN	0.956 3	0.956 9	0.959 0	0.957 9

Research on network traffic classification based on machine learning and deep learning

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