基于大数据技术的片上网络静态漏洞检测系统研究  

Research on NoC static vulnerability detection system based on big data technology

作　　者：李杰 曹鹏飞[1] 杨君[1] LI Jie;CAO Pengfei;YANG Jun(College of Computer Science and Engineering,Sanjiang University,Nanjing 210012,China)

机构地区：[1]三江学院计算机科学与工程学院

出　　处：《现代电子技术》2019年第21期77-81,共5页Modern Electronics Technique

基　　金：江苏省教育厅自然基金项目(17KJD520007);江苏省现代教育技术重点课题(2017-R-59068);三江学院虚拟仿真实验项目(XL2018002);三江学院教改项目(J18073)~~

摘　　要：为减轻静态漏洞体对计算机片上网络造成的序列化运行危害,设计一种基于大数据技术的片上网络静态漏洞检测系统。利用Hadoop检测框架,定向规划片上网络漏洞处理模块、静态漏洞评估模块的物理运行位置,实现新型检测系统的硬件运行环境搭建。在此基础上,采集漏洞信息的入侵行为,并以此为标准编写大数据检测函数,在相关组织设备的促进下,构建片上网络的静态漏洞行为链,实现新型检测系统的软件运行环境搭建。结合基础硬件条件,完成基于大数据技术的片上网络静态漏洞检测系统研究。对比实验结果显示,与hook系统相比,应用新型漏洞检测系统后,计算机片上网络的平均容错率达到90%,单位时间内出现的漏洞总量不超过3.0×10^9TB,序列化运行危害得到有效缓解。In order to reduce the serialization running harm caused by static vulnerability to computer network-on-chip(NoC),a static vulnerability detection system based on big data technology is designed.The physical operation location of the NoC vulnerability processing module and the static vulnerability assessment module is planned,and the hardware operation environment of the new detection system is built by means of Hadoop detection framework.On this basis,the vulnerability intrusion behavior information is acquired as a standard to compile big data detection functions.With the promotion of related equipments,the NoC static vulnerability behavior chain and software running environment of the new detection system are constructed,and the research of NoC static vulnerability detection system based on the big data technology is completed in combination with the basic hardware conditions.The experimental results show that,in comparison with Hook system,the new vulnerability detection system′s average fault tolerance rate of the computer NoC reaches 90%,the total number of vulnerabilities in unit time does not exceed 3.0×10^9 TB,and the serialization operation hazard is effectively alleviated.

关 键 词：大数据技术 网络漏洞 检测系统 漏洞行为链 入侵信息采集 检测函数 

分 类 号：TN915.08[电子电信—通信与信息系统] 34[电子电信—信息与通信工程] TP309[自动化与计算机技术—计算机系统结构]