Journal on Communications ›› 2018, Vol. 39 ›› Issue (1): 14-23.doi: 10.11959/j.issn.1000-436x.2018018

• Papers • Previous Articles     Next Articles

Network traffic classification method basing on CNN

Yong WANG1,2,3,Huiyi ZHOU2,3,Hao FENG1(),Miao YE3,4,Wenlong KE2   

  1. 1 School of Computer Science and Information Security,Guilin University of Electronic Technology,Guilin 541004,China
    2 School of Information and Communication,Guilin University of Electronic Technology,Guilin 541004,China
    3 Key Laboratory of Cognitive Radio and Information Processing,Guilin University of Electronic Technology,Guilin 541004,China
    4 Information Science and Technology,Guilin University of Industrial Technology,Guilin 541004,China
  • Revised:2017-12-19 Online:2018-01-01 Published:2018-02-07
  • Supported by:
    The National Natural Science Foundation of China(61662018);The National Natural Science Foundation of China(61661015);Project Funded by China Postdoctoral Foundation(2016M602922XB);The Natural Science Foundation of Guangxi Autonomous Region(2016GXNSFAA380153);Innovation Project of Guest Graduate Education(2018YJCX53);Innovation Project of Guest Graduate Education(2018YJCX20);Foundation of Guilin University of Technology(GUTQDJJ20172000019)

Abstract:

Since the feature selection process will directly affect the accuracy of the traffic classification based on the traditional machine learning method,a traffic classification algorithm based on convolution neural network was tailored.First,the min-max normalization method was utilized to process the traffic data and map them into gray images,which would be used as the input data of convolution neural network to realize the independent feature learning.Then,an improved structure of the classical convolution neural network was proposed,and the parameters of the feature map and the full connection layer were designed to select the optimal classification model to realize the traffic classification.The tailored method can improve the classification accuracy without the complex operation of the network traffic.A series of simulation test results with the public data sets and real data sets show that compared with the traditional classification methods,the tailored convolution neural network traffic classification method can improve the accuracy and reduce the time of classification.

Key words: network traffic classification, convolutional neural network, normalized, feature selection

CLC Number: 

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