Computer Science ›› 2023, Vol. 50 ›› Issue (5): 382-389.doi: 10.11896/jsjkx.220400134

• Information Security • Previous Articles    

Multi-source Fusion Network Security Situation Awareness Model Based on Convolutional Neural Network

CHANG Liwei1,2, LIU Xiujuan1, QIAN Yuhua2, GENG Haijun3, LAI Yuping4   

  1. 1 School of Information,Shanxi University of Finance and Economics,Taiyuan 030006,China
    2 Institute of Big Data Science and Industry,Shanxi University,Taiyuan 030006,China
    3 School of Automation and Software,Shanxi University,Taiyuan 030006,China
    4 School of Cyberspace Security,Beijing University of Posts and Telecommunications,Beijing 100876,China
  • Received:2022-04-13 Revised:2022-07-31 Online:2023-05-15 Published:2023-05-06
  • About author:CHANG Liwei,born in 1986,Ph.D,associate professor,is a member of China Computer Federation.His main research interests include quantum secure communication and network security situation awareness.
  • Supported by:
    Natural Science Foundation of Shanxi Province,China(20210302124290),Planning Subject for the 14th Five Year Plan of Education Sciences of Shanxi Province,China(GH-21600),Key R&D Program(International Science and Technology Cooperation Project) of Shanxi Province,China(201903D421003) and National Natural Science Foundation of China(62002210).

Abstract: For accurately calculating security situation of the whole network,a network security situation awareness model with five core elements is elaborated,which are traffic detection,attribute extraction,decision engine,multi-source fusion and situation assessment.In the traffic detection module,the network traffic detector and the intrusion detection detector are taken as a tool to grab the basic characteristics of traffic and malicious activity characteristics respectively; in the attribute extraction module,with the aim of precisely extracting key attributes,alarm messages,alarm types and connection characteristics,which contribute to describe malicious activities,are the center of attention; in the decision engine module,the key attribute data from attribute extraction is utilized as input,and CNN as an engine is employed to identify various kinds of attacks; in the multi-source fusion module,exponential weighted D-S fusion algorithm is used to effectively integrate the output of each decision engine to improve the identification rate of attack types; in the situation assessment module,in virtue of weight coefficient theory the threat levels are quantified,the hierarchical analysis method is applied to exactly get security situation of the whole network.Experimental results show that,there is a great difference in identifying varieties of attacks for different detectors,the proposed multi-source fusion algorithm can improve the accuracy of attack identification which can reach up to 92.76%,in such accuracy index our results are better than most research achievements,and the improvement of accuracy makes a great impact on accurately calculating and intuitively reflecting security situation of the whole network by means of hierarchical analysis method.

Key words: Network security situation awareness, Attack identification, Convolutional neural network, Multi-source fusion algorithm, Hierarchical analysis method

CLC Number: 

  • TP393
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