Computer Science ›› 2021, Vol. 48 ›› Issue (11A): 592-596.doi: 10.11896/jsjkx.201100170

• Information Security • Previous Articles     Next Articles

Network Intrusion Detection System Based on Multi-model Ensemble

MA Lin, WANG Yun-xiao, ZHAO Li-na, HAN Xing-wang, NI Jin-chao, ZHANG Jie   

  1. Information and Telecommunication Company,State Grid Shandong Electric Power Company,Jinan 250000,China
  • Online:2021-11-10 Published:2021-11-12
  • About author:MA Lin,born in 1991,postgraduate,intermediate engineer.Her main research interests include network and information security.
    WANG Yun-xiao,born in 1991,postgraduate,intermediate engineer.His main research interests include network and information security.
  • Supported by:
    Project of State Grid Shandong Electric Power Company(520627190059).

Abstract: The network intrusion detection system (NIDS) is widely used in the construction of network security.It can effectively identify the potential behaviors that endanger network security.In order to obtain more accurate and efficient network intrusion detection results,a network intrusion detection system based on multi-model ensemble is proposed.The system integrates Linear Support Vector Machines (Linear SVM),Residual Networks (NETS) and Temporal Convolutional Network (TCN) by using Bagging algorithm to detect the network intrusion.Intrusion detection data in experiments are 99809 web log data and AWIDof work equipment in State Grid Shandong Electric Power Companyas its public data sets.This system is compared with the single use Linear SVM,ResNets,TCN this three model.The experimental results show that by using multi-model ensemble algorithm,integrating the advantages of each model,the overall accuracy of this system reaches up to 99.24% and is 7.95% more than TCN.In addition,the system not only has a very high accuracy rate,the alarm rate is also as low as 0.07%,which is consistent with the requirements of network security protection system,and successfully realizes more accurate and efficient network intrusion detection.

Key words: Deep neural network, Intrusion detection, Mmulti-model ensemble, Network security protection

CLC Number: 

  • TP393.0
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