摘要
为了提高信息安全风险评价的准确性,提出一种基于灰色关联和神经网络的信息安全风险评价模型.首先建立信息安全风险评价指标体系,然后采用灰色联法分析评价指标之间的关联度,删除冗余评价指标,最后采用RBF神经网络建立信息安全风险评价模型,并对某企业信息系统的信息安全风险进行了评价.结果表明,该模型可以有效地去除一些冗余评价指标,大幅度降低了RBF神经网络的输入向量数,提高了信息安全风险评价的效率,获得较对比模型更高的信息安全风险评价精度.
In order to improve the accuracy of information security risk assessment, this paper puts forward a novel information security risk assessment model based on grey relation analysis and neural network. First of all, index system of information security risk evaluation is built, and secondly, grey relation analysis method is used to analyze relation between the evaluation index to remove some redundant evaluation indexes, finally,RBF neural network is used to establish information security risk assessment model and the performance of security risk evaluation is test by an enterprise information system. The results show that the proposed model can remove useless evaluation indexes to reduces the number of input vectors for RBF neural network,improve the efficiency, and assessment accuracy is higher than that other models.
出处
《内蒙古师范大学学报（自然科学汉文版）》
CAS
北大核心
2016年第2期166-169,173,共5页
Journal of Inner Mongolia Normal University(Natural Science Edition)
基金
山东省科技发展计划(软科学)项目(2013RKA16010)
关键词
信息系统
灰色关联
神经网络
风险评价
information system
gray relation analysis
neural network
risk assessment
