计算机工程

• 安全技术 • 上一篇    下一篇

面向网络监测预警的海量知识存储研究

饶志宏 1,刘杰 1,2,陈剑锋 1,2   

  1. (1.中国电子科技集团公司第三十研究所,成都 610041;2.中国电子科技集团公司网络空间安全技术重点实验室,成都 610041)
  • 收稿日期:2017-11-29 出版日期:2018-03-15 发布日期:2018-03-15
  • 作者简介:饶志宏(1970—),男,研究员,主研方向为网络空间安全;刘杰,高级工程师;陈剑锋,研究员。
  • 基金项目:
    国家重点研发计划项目(2016YFB0801300)。

Research on Massive Knowledge Storage for Network Monitoring and Early Warning

RAO Zhihong  1,LIU Jie  1,2,CHEN Jianfeng  1,2   

  1. (1.The 30th Institute of China Electronics Technology Group Corporation,Chengdu 610041,China; 2.Cyberspace Security Technology Key Laboratory of China Electronics Technology Group Corporation,Chengdu 610041,China)
  • Received:2017-11-29 Online:2018-03-15 Published:2018-03-15

摘要: 海量知识的高效管理是网络监测预警发挥效能的前提。为此,提出一种基于图数据库的大规模资源描述框架(RDF)数据存储方法。根据RDF数据的图模型特征,基于启发式的贪婪策略对数据集进行分割,包括子图生成阶段和子图划分阶段,同时采用热点数据动态复制删除的方式实现动态数据流的负载均衡。在3个不同数据集上的对比实验表明,该方法的存储性能优于基于关系型数据库的方法。

关键词: 网络监测预警, 图数据库, 资源描述框架数据存储, 数据集划分, 负载均衡

Abstract: Efficient management of massive knowledge is the prerequisite for the effectiveness of network monitoring and early warning.Aiming at this problem,a large-scale Resource Description Framework(RDF) data storage approach is proposed based on graph database.By leveraging the graph characteristics of the RDF data,it partitions the RDF dataset based on a heuristic greedy strategy.Dataset partitioning contains the subgraph generation stage and subgraph partitioning stage.It also considers the dynamic property of the data flow.Meanwhile,it uses a dynamic mechanism for replicating and deleting the hot spot data to achieve load balancing of dynamic dataflow.The proposed approach is compared based on relational databases on three different datasets.The experimental results show that it is obviously better than the approach based on relational database.

Key words: network monitoring and early warning, graph database, Resource Description Framework(RDF) data storage, dataset partitioning, load balancing

中图分类号: