摘要
研究一种有效的面向大数据的信息传输隐性风险预测方法对保障网络信息安全具有重要意义。由于面向大数据的信息传输系统在组织结构和功能等方面存在差异性,当前的预测方法已经不能满足隐性风险预测的实际需要,提出一种基于灰色模糊理论的信息传输隐性风险预测方法,对面向大数据的信息传输隐性风险预测过程中的资产、威胁性和脆弱性三个基本要素进行准确识别和赋值,得到面向大数据的信息传输隐性风险值;在分析面向大数据的信息传输隐性风险因素的基础上,以灰色理论与模糊集理论为依据,建立了一套比较全面的指标体系对信息传输隐性风险进行预测;并将最大隶属度原则和最小灰色度原则应用到预测过程中,构建了信息传输隐性风险的灰色模糊综合预测模型,对风险等级进行划分,输出预测结果。通过仿真实例与当前预测方法的对比,证明了所提方法将定量分析与定性分析进行了有效结合,具有更高的准确性,也更加贴近实际结果。
A prediction method for recessive risk in information transmission based on gray fuzzy theory was proposed. Three basic elements, such as asset, threat and fragility, in the process of predicting recessive risk of information transmission based on big data were accurately identified and evaluated to obtain the recessive risk value of information transmission. According to the analysis of recessive risk of information transmission for big data, a set of comprehensive index system was established based on gray theory and fuzzy set theory to predict the recessive risk of information transmission. The principles of the maximum membership degree and the minimum gray degree were applied to the process of prediction, so as to build the gray fuzzy comprehensive prediction model of recessive risk in information transmission. Finally, the risk level was divided. Thus, the prediction result was output. Through the comparison between the simulation example and the current prediction method, the proposed method combines quantified analysis with qualitative analysis is effective. It has high accuracy, which is close to actual results.
作者
孔德生
宁勇
KONG De -sheng, NING Yong(Affiliated Hospital, Jilin Medical College, Jilin Jilin 132013,China)
出处
《计算机仿真》
北大核心
2018年第10期410-414,共5页
Computer Simulation
关键词
大数据
信息传输
隐性风险
预测
Big data
Information transfer
Recessive risk
Prediction
