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Classification Method of Encrypted Traffic Based on Deep Neural Network

Jing WanLibing WuYouhua XiaJianzong HuZhenchang XiaRui ZhangMin Wang

School of Computer Science, Wuhan University

摘要:With the widespread use of network traffic encryption technology, the traditional traffic classification method has gradually become invalid, which increases the difficulty of network management and poses a serious threat to network security. This paper analyzes the traffic encrypted and transmitted by VPN and explores its classification method. By extracting the timing characteristics of the encrypted traffic, the classification model of the deep neural network was used to classify the traffic of seven different categories in the encrypted traffic, and compared with the commonly used naive Bayesian classification algorithm. At the same time, the batch size that affects the training of deep neural network models was studied. Experiments show that the classification ability of encrypted traffic classification model based on deep neural network is much better than the naive Bayesian method. During training, the batch size has different effects on the deep neural network model. When the batch size is 40, the deep neural network model has the best classification ability.
会议名称:

2019国际计算机前沿大会

会议时间:

2019-09-20

会议地点:

中国广西桂林

  • 专辑:

    信息科技

  • 专题:

    互联网技术; 自动化技术

  • 分类号:

    TP183;TP393.06

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