Computer Science ›› 2021, Vol. 48 ›› Issue (6A): 459-463.doi: 10.11896/jsjkx.200600161

• Information Security • Previous Articles     Next Articles

Research on Intrusion Detection Classification Based on Random Forest

CAO Yang-chen1, ZHU Guo-sheng1, QI Xiao-yun2, ZOU Jie1   

  1. 1 School of Computer and Information Engineering,Hubei University,Wuhan 430062,China
    2 School of Chemistry and Chemical Engineering,Hubei University,Wuhan 430062,China
  • Online:2021-06-10 Published:2021-06-17
  • About author:CAO Yang-chen,born in 1996,postgraduate.Her main research interests include machine learning and network traffic analysis.
    ZHU Guo-sheng,born in 1972,Ph.D,professor.His main research interestsinclude next-generation internet and software-defined networks.
  • Supported by:
    CERNET Innovation Project and Special Operation Education and Training System Based on Cloud VR and IPv6(NGII20180507).

Abstract: In order to effectively detect the attack behavior of the network,the machine learning method are widely used to classify different types of network intrusion detection.The traditional decision tree methods usually use a single model to training data,which is prone to generalization errors and is prone to over-fitting.To solve this problem,this paper introduces the idea of parallel integrated learning,and proposes an intrusion detection model based on random fo-rest.Since each decision tree in the random fo-rest has decision-making power,it can improve the accuracy of classification very well.By using the NSL-KDD data set to train and test the intrusion detection model,the experimental results show that the accuracy rate can reach 99.91%,which shows that the model has a very good intrusion detection classification effect.

Key words: Decision tree, Intrusion detection, Machine learning, Random forest

CLC Number: 

  • TP181
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