Computer Science ›› 2019, Vol. 46 ›› Issue (6): 148-152.doi: 10.11896/j.issn.1002-137X.2019.06.022

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Network System Risk Assessment Model with Optimal Weights

ZHANG Jie-hui1, PAN Chao2, ZHANG Yong1   

  1. (Network and Computation Center,Huazhong University of Science and Technology,Wuhan 430074,China)1
    (School of Information and Communication Engineering,Hubei University of Economics,Wuhan 430205,China)2
  • Received:2018-10-12 Published:2019-06-24

Abstract: Network system risk is affected by many factors,and has strong time-varying and non-linear characteristics.As a result,a single model cannot fully describe the characteristics of network system risk change.The traditional combination model cannot accurately describe the contribution of each model on the final evaluation results for network system risk by determining the weight of the model according to the network system risk assessment errors,causing the poor accuracy of network system risk assessment.In order to improve the effect of network system risk assessment,a network system risk assessment model with optimal weights was designed.Firstly,different models are used to evaluate the network system risk from different perspectives,and the prediction results of a single model is obtained.Then,the network system risk assessment results of a single model are taken as an evidence body.According to the improved evidence theory,the evidence body is fused,and then the final evaluation of network system risk is obtained.Finally,the proposed method is compared with other network system risk assessment methods.The test results show that the model can accurately evaluate the network system risk and reflect the changing characteristics of the network security situation.The evaluation accuracy is obviously better than other network system risk assessment methods,and more ideal network system risk assessment results can be obtained.

Key words: Changing situation, Evaluation method, Evidence body, Network security, Neural network, Support vector machine

CLC Number: 

  • TP393
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