摘要
工业互联网安全问题日益突出,对工业互联网安全漏洞知识库的深入研究是解决问题的关键。为解决漏洞数据利用价值低、关联分析手段欠缺、可视化程度不足等问题,以工业互联网安全漏洞库为基础,提出了构建工业互联网安全漏洞知识图谱的方法,通过原始数据信息提取、关联关系分析、数据存储等手段,将知识图谱导入到Neo4j图数据库,以实现高效存储、查询。从时间维度、空间维度、关联关系维度进行知识图谱的分析,将查询结果进行了可视化展现。结果表明:提出的方法可以有效、直观地展现工业互联网安全漏洞数据的自身属性与关联关系,实现漏洞数据内在价值的深度挖掘。
The problem of industrial Internet security is becoming more and more prominent.The key to solve the problem is to deeply study the knowledge base of industrial internet security vulnerabilities.In order to solve the problems of low value of exploiting vulnerable data,insufficient means of association analysis and insufficient degree of visualization,based on the data of industrial Internet security vulnerability database,a method of constructing knowledge graph of industrial Internet security vulnerabilities is put forward.Knowledge graph is imported into Neo4j graph database by means of original data information extraction,association analysis and data storage,so as to achieve efficient storage and query.From the time dimension,spatial dimension and correlation dimension,the knowledge graph is analyzed,and the query results are visualized.The results show that the proposed method can effectively and intuitively show the attributes and relationship of vulnerability data in industrial Internet,and realize the deep mining of the intrinsic value of vulnerability data.
作者
陶耀东
贾新桐
吴云坤
Tao Yaodong;Jia Xintong;Wu Yunkun(Beijing Jiaotong University,Beijing 100044,China;Industrial Control System Security National Local Joint Engineering Laboratory,Beijing 100015,China;Qi An Xin Technology Group Co.,Ltd.,Beijing 100015,China)
出处
《信息技术与网络安全》
2020年第1期6-13,18,共9页
Information Technology and Network Security
关键词
工业互联网
漏洞库
知识图谱
关联关系
industrial Internet
vulnerability database
knowledge graph
correlation
