基于贝叶斯推理的数据云存储安全风险感知研究
Research on security risk perception of data cloud storage based on Bayesian inference
黄丽芳1
作者信息
- 1. 闽南理工学院 信息管理学院,福建 石狮 362700
- 折叠
摘要
为解决以固定阈值为基础的网络数据存储安全风险、感知方式存在明显滞后的问题,提出基于贝叶斯推理的网络数据云存储安全风险感知方法.定性分析网络数据云存储安全风险的影响因素,对影响因素展开关联分析以及重组,得到精准的网络数据云存储安全风险数据,包括技术类风险、管理类风险、法规类风险等.构建基于贝叶斯推理的云存储安全风险感知模型,利用评分函数修正贝叶斯网络拓扑结构,通过量化分析获取安全风险感知值,实现网络数据云存储安全风险感知.实验结果表明,所提方法的网络数据云存储安全风险漏报率小于0.2,误报率最大值仅为1.1,感知值和实际值差距较小,安全风险感知时间较短,网络数据云存储安全风险感知效果好.
Abstract
To solve the problem of significant lag in the perception of network data storage security risks based on fixed thresholds,a method for network data cloud storage security risk perception based on Bayesian inference is proposed.Quali-tative analysis is conducted on the influencing factors of network data cloud storage security risks,and correlation analysis and recombination are carried out to obtain accurate network data cloud storage security risk data,including technical risks,management risks,regulatory risks,etc.A cloud storage security risk perception model based on Bayesian inference is constructed,and the Bayesian network topology is modified using a scoring function.The security risk perception value is obtained through quantitative analysis to achieve network data cloud storage security risk perception.The experimental resultsshow that the network data cloud storage security risk omission rate of the proposed method is below 0.2,the maxi-mum false alarm rate is only 1.1,and the difference between the perceived value and the actual value is small.The security risk perception time is short,and the network data cloud storage security risk perception effect is good.
关键词
贝叶斯推理/网络数据/云存储安全/风险感知/随机森林/评分函数Key words
Bayesian inference/Network data/Cloud storage security/Risk perception/Random forest/Scoring function引用本文复制引用
基金项目
闽南理工学院科技创新团队项目(23XTD114)
出版年
2024
NETL
NSTL
维普
万方数据