Computer Science ›› 2021, Vol. 48 ›› Issue (12): 349-356.doi: 10.11896/jsjkx.210400227

• Information Security • Previous Articles     Next Articles

Network Security Situation Based on Time Factor and Composite CNN Structure

ZHAO Dong-mei1,2, SONG Hui-qian1, ZHANG Hong-bin3   

  1. 1 College of Computer and Cyber Security,Hebei Normal University,Shijiazhuang 050024,China
    2 Hebei Key Laboratory of Network and Information Security,Hebei Normal University,Shijiazhuang 050024,China
    3 School of Information Science and Engineering,Hebei University of Science and Technology,Shijiazhuang 050018,China
  • Received:2021-04-21 Revised:2021-09-05 Online:2021-12-15 Published:2021-11-26
  • About author:ZHAO Dong-mei,born in 1966,Ph.D,professor,Ph.D supervisor,is a senior member of China Computer Federation.Her main research interests include network information security and computer application.
  • Supported by:
    National Natural Science Foundation of China(61672206),Central Guide Local Science and Technology Development Fund Project(216Z0701G), Key Research and Development Program of Hebei Province(20310701D) and Natural Science Foundation of Hebei Province(F2019205163).

Abstract: In order to solve the problem of low accuracy of traditional network security situation awareness research methods in the case of complex network information,combined with deep learning,this paper proposes a network security situation assessment model based on time factor and composite CNN structure,which combines volume integral solution technology and deep separable technology to form a four layer series composite optimal unit structure.The one-dimensional network data are transformed into two-dimensional matrix and loaded into the neural network model in the form of gray value,so as to give full play to the advantages of convolution neural network.In order to make full use of the time-series relationship between data,time factor is introduced to form fusion data,which makes the network to learn the original data and fusion data with time-series relationship at the same time,the feature extraction ability of the model is increased,the spatial mapping of time-series data is established by using time factor and point convolution,and the integrity of the model structure is increased.Experimental results show that the accuracy of the proposed model on two datasets is 92.89% and 92.60% respectively,which is 2%~6% higher than randomfo-rest and LSTM algorithm.

Key words: CNN, Convolution decomposition, Depthwise separable convolution, Situational awareness, Time factor

CLC Number: 

  • TP393
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