ISSN 1000-1239 CN 11-1777/TP

计算机研究与发展 ›› 2016, Vol. 53 ›› Issue (5): 1009-1017.doi: 10.7544/issn1000-1239.2016.20150065

• 信息安全 • 上一篇    下一篇

基于粗糙集的漏洞属性约简及严重性评估

付志耀,高岭,孙骞,李洋,高妮   

  1. (西北大学信息科学与技术学院 西安 710127) (fuzhiyao@stumail.nwu.edu.cn)
  • 出版日期: 2016-05-01
  • 基金资助: 
    国家自然科学基金项目(61373176);国家科技支撑计划基金项目(2013BAK01B02);陕西省重大科技创新项目(2012ZKC05-2);西安市技术转移促进工程(高校类)基金项目(CXY1440-8);西北大学科学研究基金项目(12NW07)

Evaluation of Vulnerability Severity Based on Rough Sets and Attributes Reduction

Fu Zhiyao, Gao Ling, Sun Qian, Li Yang, Gao Ni   

  1. (School of Information Science and Technology, Northwest University, Xi’an 710127)
  • Online: 2016-05-01

摘要: 计算机漏洞是危害网络安全的重大隐患,可以利用系统配置不当、系统设计缺陷或是软件的bug等对系统攻击.由于产生漏洞有多种因素,使得与漏洞相关的属性有很多,难以客观筛选强关联属性.而且在不依赖专家经验或是先验知识的基础上,确定属性权重的客观标准也是一个困难的问题.提出一种新的漏洞评估方法RAR,首先采用粗糙集理论中改进的可辨识矩阵算法,得到约简的漏洞强关联属性集;进而利用属性综合评价系统理论评估漏洞的严重性;最终获得二元组表示漏洞的定性评估值和定量评估值.实验结果体现该方法避免了主观选择漏洞强关联属性集和依赖专家先验知识,在漏洞属性约简和属性权重的计算上获得了满意的效果,对漏洞的定性分析和定量分析是准确有效的.

关键词: 漏洞, 网络安全, 粗糙集, 属性约简, 漏洞评估

Abstract: Computer vulnerability is a major hidden danger which endangers the safety of the network, and will attack the system by system configuration mistakes, system design flaws or software bugs. Due to a variety of factors which can produce vulnerability, there are many attributes associated with vulnerability, and it is difficult to shift attributes which are more relevant. It is also a hard problem to calculate attribute weights objectively which doesn’t depend on expert experience or prior knowledge. A new method named RAR of vulnerability assessment is proposed to shift vulnerability attributes and evaluate severity objectively. The attributes reduction for decision-making of vulnerability assessment is found depended on the discriminate matrix in rough sets theory. Then evaluate the vulnerability severity based on attributes comprehensive evaluation system theory. Finally we can get a binary group to represent qualitative evaluation and quantitative evaluation value of vulnerability. The result shows this method avoids the subjective choice for vulnerability attributes and the dependence of experts prior knowledge, and it satisfies for attributes reduction and attribute weights. And it is also accurate and effective for qualitative analysis and quantitative analysis of the vulnerability.

Key words: vulnerability, network security, rough sets, attributes reduction, assessment of vulnerability

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