摘要
在大数据背景下,常用的防窃取方法检测率较低,导致防窃取效果较差。为了解决该问题,提出基于图自同构的链路网络敏感数据防窃取方法。通过分析敏感数据被攻击过程,对被窃取数据进行初步检测。根据初步检测结果,通过被动响应方式,提取敏感数据特征,根据特征值,建立图自同构模型,使数据能够被描述,以此实现链路网络敏感数据防窃取。由实验调试结果可知,该方法最高检测率可达到98%,为用户信息安全提供保障。
The previous anti-theft methods have the problem of low detection rate in large data background,which leads to poor anti-theft effect.To solve this problem,a link network sensitive data anti-theft method based on graph automorphism is proposed.By analyzing the attack process of sensitive data,the stolen data is preliminarily detected.According to the preliminary test results,the characteristics of sensitive data are extracted by passive response,and the graph automorphism model is established according to the eigenvalues,so that the data can be described,so as to realize the link network sensitive data anti-theft.According to the experimental debugging results,the highest detection rate of this method can reach 98%,which provides security for users′ information security.
作者
孟小冬
MENG Xiaodong(School of Computer,Hulunbuir University,Hulunbuir 021000,Inner Mongolia,China)
出处
《西安工程大学学报》
CAS
2019年第2期212-217,共6页
Journal of Xi’an Polytechnic University
基金
内蒙古自治区自然科学基金项目(2017MS(LH)0682)
关键词
大数据
链路网络
敏感数据
防窃取
检测率
big data
link network
sensitive data
anti-theft
detection rate
