摘要
针对单一模型的信息安全风险估计精度低难题,综合利用自回归差分滑动平均和相关向量机优势,提出一种基于ARIMA-RVM的信息安全风险估计模型。首先收集信息系统安全状态的历史时间序列数据,并采用局部均值分解算法对历史时间序列数据进行分解,产生若干个分量,然后对低频分量和余项采用自回归差分滑动平均进行估计,对频率值较大、频率变化波动性较大的分量定为高频分量,采用相关向量机进行估计,最后将各个分量的估计结果相叠加得到最终信息安全风险估计值。实验结果表明,本文模型可以准确描述信息系统的安全变化趋势,提高了信息安全风险估计的精度,具有较高的实际应用价值。
The single estimation models of information security risk have low accuracy n problems,comprehensive utilization of auto regressive integrated moving average and relevance vector machine advantage,this paper puts forward a information security risk estimation model based on ARIMA-RVM.Firstly,the historical time series data are collected for the information system security status,and t local mean decomposition algorithm is used to decompose time series data and generate some component,secondly,the low-frequency components are modeled by auto regressive integrated moving average while frequency change value is large,volatile component are modeled by relevance vector machine,finally,the estimate results of components are added to get final results of information security risk.Experimental results show that the proposed model can accurately describe the change trend of information system,improve the information security risk estimation accuracy and has higher practical application value.
出处
《激光杂志》
北大核心
2015年第4期155-159,共5页
Laser Journal
基金
贵州科技厅计划(201499231)
关键词
信息安全
风险估计
局部均值分解
相关向量机
自回归差分滑动平均
information security
risk estimation
local mean decomposition
auto regressive integrated moving average
relevance vector machine
