JOURNAL OF LIGHT INDUSTRY

CN 41-1437/TS  ISSN 2096-1553

Volume 35 Issue 4
July 2020
Article Contents
WANG Xuanli, ZHANG Anlin, HUANG Daoying, et al. Network traffic classification analysis of different machine learning algorithms in SDN environment[J]. Journal of Light Industry, 2020, 35(4): 96-102. doi: 10.12187/2020.04.013
Citation: WANG Xuanli, ZHANG Anlin, HUANG Daoying, et al. Network traffic classification analysis of different machine learning algorithms in SDN environment[J]. Journal of Light Industry, 2020, 35(4): 96-102. doi: 10.12187/2020.04.013 shu

Network traffic classification analysis of different machine learning algorithms in SDN environment

  • Received Date: 2020-03-31
  • In order to compare and analyze the network traffic classification effect of different machine learning algorithms in the software defined network (SDN) environment, the Moore dataset was balanced, and four classic machine learning algorithms including KNN, random forest (RF), support vector machine (SVM) and gradient lifting decision tree (GBDT) were supported on the machine learning platform RapidMiner to select different classification features for classification experiments. Experimental results showed that compared with the other three algorithms, the GBDT algorithm could obtain better classification results in a shorter time.
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