Computer Science

Computer Science ›› 2023, Vol. 50 ›› Issue (11A): 220800120-7.doi: 10.11896/jsjkx.220800120

• Network & Communication • Previous Articles     Next Articles

Noise Tolerant Algorithm for Network Traffic Classification Method

MA Jiye, ZHU Guosheng, WEI Cao, ZENG Yuxuan   

  1. School of Computer and Information Engineering,Hubei University,Wuhan 430062,China
  • Published:2023-11-09
  • About author:ZHU Guosheng,born in 1972,Ph.D,professor,is a member of China Computer Federation.His main research interests include future networks and so on.

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Abstract: Aiming at the problem that the correctness of the sample labels in the traditional machine learning-based network traffic classification method will directly affect the accuracy of the results,a noise-tolerant network traffic classification method is proposed,which is based on the deep residual network method.After normalization and data enhancement,the data is mapped into a grayscale image,and the sample labels are added to different degrees of noise.Then,based on the Res2Net deep residual neural network,a dimensional module suitable for the interference of network traffic noise is designed,and a deep neural network model suitable for traffic label noise tolerance is constructed.Experimental results on public datasets show that compared with the traditional noise-tolerant classification algorithm,the improved deep residual neural network improves the classification accuracy under different noise rates,and the improvement is more significant at high noise rates.

Key words: Noise tolerant, Deep learning, Residual learning, Network traffic classification, Label noise, Normalized

CLC Number: 

  • TP393
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