摘要
通过研究网络风险传播途径和规律,提出一种RiskRank网络风险传播分析方法。通过计算网络节点间相似关系和临近关系,以构建网络风险传播图谱,并基于随机游走方法迭代计算网络风险传播模型,以动态分析网络风险传播过程并量化评估网络风险程度,最后采用密度聚类算法识别高风险簇,通过隔离高风险簇以控制安全态势。实验结果表明,提出的RiskRank网络风险传播模型的准确率为97.4、精度为98.1%、召回率为86.4%。
This paper proposes a RiskRank method to analyze the network risk propagation by studying the path and law of the network risk propagation.By computing the similarity and proximity between network nodes,a graph of network risk propagation is built,based on which a network risk propagation model is trained by iterations of random walk.The model is used to dynamically analyze the process of network risk propagation and quantitively evaluate the risk of network nodes.A high-risk clustering method is proposed based on the density clustering algorithm to isolate the high-risk area,thus controlling the security risk.The experimental results show that the accuracy,the precision and the recall of the RiskRank model is 97.4%,98.1%and 86.4%,respectively.
作者
张之刚
常朝稳
韩培胜
侯湘
ZHANG Zhigang;CHANG Chaowen;HAN Peisheng;HOU Xiang(Cryptography Engineering Institute,Strategic Support Force Information Engineering University,Zhengzhou 450000,P.R.China;DaTang Centrol-China Electric Power Test Research Institute,Zhengzhou 450006,P.R.China;Journal Department,Chongqing University,Chongqing 400044)
出处
《重庆大学学报》
CSCD
北大核心
2021年第9期132-138,共7页
Journal of Chongqing University
基金
国家自然科学基金资助项目(61572517)
重庆市自科基金资助项目(cstc2021jcyj-msxm4008)。
关键词
网络风险评估
风险传播图谱
随机游走
密度聚类算法
network risk evaluation
risk propagation graph
random walk
density clustering algorithm
