基于风险数据挖掘追踪的云计算网络漏洞检测技术

发布时间：2018-05-11 03:43

本文选题：风险数据 + 挖掘　；参考：《科技通报》2016年05期

【摘要】：在对云计算网络漏洞进行检测的过程中,涉及的数据量巨大,传统方法依据先验知识建立云计算网络漏洞库,通过和漏洞库的匹配实现漏洞检测,实现过程非常复杂,且建立的漏洞库也存在局限性,检测精度低,因此,提出一种基于风险数据挖掘追踪的云计算网络漏洞检测技术。将特征相关性看作是计算数据差异的依据对风险数据进行初聚类,将和风险数据有较大差异的正常数据剔除,完成初步的处理。引入模糊关联规则,依据风险数据属性关联规则,对经初聚类处理后的风险数据进行挖掘追踪,构成云计算网络漏洞数据库,通过隶属度分布函数对漏洞数据库的连续属性进行模糊处理,依据模糊关联规则挖掘构建云计算网络存在漏洞状态时的关联规则集,用相似度对云计算网络当前状态和存在漏洞状态的背离程度进行描述,实现云计算网络的漏洞检测。仿真实验结果表明,所提方法具有很高的检测精度和检测效率。
[Abstract]:In the process of detecting cloud computing network vulnerabilities, the amount of data involved is huge. The traditional method establishes cloud computing network vulnerability library based on prior knowledge, and realizes vulnerability detection by matching vulnerability library, which is very complicated. The vulnerability library also has limitations and low detection accuracy. Therefore, a cloud computing network vulnerability detection technology based on risk data mining tracing is proposed. The feature correlation is regarded as the basis of calculating the difference of data. The risk data is first clustered, and the normal data which is different from the risk data is eliminated, and the preliminary processing is completed. The fuzzy association rule is introduced to mine and trace the risk data after initial clustering according to the risk data attribute association rules, which constitutes the cloud computing network vulnerability database. The continuous attributes of vulnerability database are fuzzy processed by membership degree distribution function, and the association rules set of cloud computing network is constructed according to the mining of fuzzy association rules. This paper describes the degree of deviation between the current state and the existing state of cloud computing network by similarity, and realizes the vulnerability detection of cloud computing network. The simulation results show that the proposed method has high detection accuracy and efficiency.
【作者单位】：顺德职业技术学院电子与信息工程学院;
【基金】：国家自然科学基金(编号:41072247)
【分类号】：TP393.08

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