最优化权值的网络系统风险组合评价模型

doi:10.11896/j.issn.1002-137X.2019.06.022

摘要/Abstract

摘要： 网络系统风险受众多因素影响,具有较强的时变性和非线性变化的特点,导致单一模型无法全面描述网络系统风险变化的特点。传统组合模型根据网络系统风险评价确定模型的权值,无法准确描述每一个模型对网络系统风险最终评价结果的贡献,使得网络系统风险评价的准确性差。为了改善网络系统风险评价的效果,文中设计了最优化权值的网络系统风险组合评价模型。首先利用不同模型从不同角度对网络系统风险进行评价,以得到单一模型的预测结果;然后将单一模型的网络系统风险评价结果作为证据体,根据改进证据理论对证据体进行融合,得到网络系统风险的最终评价;最后将提出的方法与其他网络系统风险评价进行了对比测试。测试结果表明,所提模型可以准确地对网络系统风险进行评价,能够反映网络系统风险的变化特点,获得更加理想的网络系统风险评价结果,且评价精度要明显优于其他网络系统风险评价模型。

关键词: 变化态势, 评价模型, 神经网络, 网络安全, 证据体, 支持向量机

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

中图分类号:

TP393

张洁卉, 潘超, 章勇. 最优化权值的网络系统风险组合评价模型[J]. 计算机科学, 2019, 46(6): 148-152. https://doi.org/10.11896/j.issn.1002-137X.2019.06.022

ZHANG Jie-hui, PAN Chao, ZHANG Yong. Network System Risk Assessment Model with Optimal Weights[J]. Computer Science, 2019, 46(6): 148-152. https://doi.org/10.11896/j.issn.1002-137X.2019.06.022

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