發(fā)布時(shí)間：2018-05-11 03:43

  本文選題：風(fēng)險(xiǎn)數(shù)據(jù) + 挖掘��； 參考：《科技通報(bào)》2016年05期

【摘要】：在對(duì)云計(jì)算網(wǎng)絡(luò)漏洞進(jìn)行檢測(cè)的過(guò)程中,涉及的數(shù)據(jù)量巨大,傳統(tǒng)方法依據(jù)先驗(yàn)知識(shí)建立云計(jì)算網(wǎng)絡(luò)漏洞庫(kù),通過(guò)和漏洞庫(kù)的匹配實(shí)現(xiàn)漏洞檢測(cè),實(shí)現(xiàn)過(guò)程非常復(fù)雜,且建立的漏洞庫(kù)也存在局限性,檢測(cè)精度低,因此,提出一種基于風(fēng)險(xiǎn)數(shù)據(jù)挖掘追蹤的云計(jì)算網(wǎng)絡(luò)漏洞檢測(cè)技術(shù)。將特征相關(guān)性看作是計(jì)算數(shù)據(jù)差異的依據(jù)對(duì)風(fēng)險(xiǎn)數(shù)據(jù)進(jìn)行初聚類,將和風(fēng)險(xiǎn)數(shù)據(jù)有較大差異的正常數(shù)據(jù)剔除,完成初步的處理。引入模糊關(guān)聯(lián)規(guī)則,依據(jù)風(fēng)險(xiǎn)數(shù)據(jù)屬性關(guān)聯(lián)規(guī)則,對(duì)經(jīng)初聚類處理后的風(fēng)險(xiǎn)數(shù)據(jù)進(jìn)行挖掘追蹤,構(gòu)成云計(jì)算網(wǎng)絡(luò)漏洞數(shù)據(jù)庫(kù),通過(guò)隸屬度分布函數(shù)對(duì)漏洞數(shù)據(jù)庫(kù)的連續(xù)屬性進(jìn)行模糊處理,依據(jù)模糊關(guān)聯(lián)規(guī)則挖掘構(gòu)建云計(jì)算網(wǎng)絡(luò)存在漏洞狀態(tài)時(shí)的關(guān)聯(lián)規(guī)則集,用相似度對(duì)云計(jì)算網(wǎng)絡(luò)當(dāng)前狀態(tài)和存在漏洞狀態(tài)的背離程度進(jìn)行描述,實(shí)現(xiàn)云計(jì)算網(wǎng)絡(luò)的漏洞檢測(cè)。仿真實(shí)驗(yàn)結(jié)果表明,所提方法具有很高的檢測(cè)精度和檢測(cè)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.
【作者單位】： 順德職業(yè)技術(shù)學(xué)院電子與信息工程學(xué)院;
【基金】：國(guó)家自然科學(xué)基金(編號(hào):41072247)
【分類號(hào)】：TP393.08

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