弱關(guān)聯(lián)規(guī)則下的聯(lián)合數(shù)據(jù)庫(kù)入侵檢測(cè)方法研究
發(fā)布時(shí)間:2019-02-14 23:32
【摘要】:聯(lián)合數(shù)據(jù)庫(kù)的入侵和普通入侵不同,其無(wú)顯著的行為特征,入侵?jǐn)?shù)據(jù)屬性差異較大,很難形成統(tǒng)一的約束規(guī)范,導(dǎo)致傳統(tǒng)的入侵檢測(cè)方法,由于通過(guò)提取入侵行為特征進(jìn)行入侵檢測(cè),無(wú)法有效且準(zhǔn)確地完成聯(lián)合數(shù)據(jù)庫(kù)的入侵檢測(cè),提出一種弱關(guān)聯(lián)規(guī)則下的聯(lián)合數(shù)據(jù)庫(kù)入侵檢測(cè)方法,通過(guò)弱關(guān)聯(lián)模式在聯(lián)合數(shù)據(jù)庫(kù)中支持程度與聯(lián)合數(shù)據(jù)庫(kù)記錄總量的比求出弱關(guān)聯(lián)模式的支持度,獲取頻繁弱關(guān)聯(lián)模式集,采用改進(jìn)的雙置信度算法對(duì)頻繁弱關(guān)聯(lián)模式集的置信度進(jìn)行計(jì)算,獲取弱關(guān)聯(lián)規(guī)則,依據(jù)弱關(guān)聯(lián)規(guī)則,通過(guò)原始聯(lián)合數(shù)據(jù)庫(kù)對(duì)分類(lèi)超平面進(jìn)行計(jì)算,采用該超平面完成聯(lián)合數(shù)據(jù)庫(kù)的整體分類(lèi),采用主成分分析方法對(duì)聯(lián)合數(shù)據(jù)庫(kù)中的操作數(shù)據(jù)進(jìn)行降維處理,通過(guò)差異分類(lèi)方法,對(duì)聯(lián)合數(shù)據(jù)庫(kù)中的操作數(shù)據(jù)特征進(jìn)行分類(lèi)操作,實(shí)現(xiàn)弱關(guān)聯(lián)規(guī)則下聯(lián)合數(shù)據(jù)庫(kù)的有效入侵檢測(cè)。實(shí)驗(yàn)表明,所提方法具有很高的準(zhǔn)確性及有效性。
[Abstract]:The intrusion of federated database is different from that of common intrusion, it has no significant behavior characteristics, and the attribute of intrusion data is different, so it is difficult to form a unified constraint standard, which leads to the traditional intrusion detection method. Because intrusion detection can not be done effectively and accurately by extracting intrusion behavior features, a joint database intrusion detection method based on weak association rules is proposed. Based on the ratio of the degree of support of the weak association schema in the joint database to the total record volume of the joint database, the support degree of the weak association pattern is obtained, and the frequent weak association pattern set is obtained. The improved double confidence algorithm is used to calculate the confidence of frequent and weak association pattern sets, and the weak association rules are obtained. According to the weak association rules, the classification hyperplane is calculated by the original joint database. The hyperplane is used to complete the whole classification of the joint database, the principal component analysis (PCA) method is used to reduce the dimension of the operation data in the joint database, and the operational data characteristics in the joint database are classified by the differential classification method. The effective intrusion detection of federated database under weak association rules is realized. Experiments show that the proposed method is accurate and effective.
【作者單位】: 瓊州學(xué)院電子信息工程學(xué)院;
【基金】:瓊州學(xué)院校級(jí)青年科學(xué)基金項(xiàng)目:(QYQN201338)
【分類(lèi)號(hào)】:TP311.13;TP393.08
[Abstract]:The intrusion of federated database is different from that of common intrusion, it has no significant behavior characteristics, and the attribute of intrusion data is different, so it is difficult to form a unified constraint standard, which leads to the traditional intrusion detection method. Because intrusion detection can not be done effectively and accurately by extracting intrusion behavior features, a joint database intrusion detection method based on weak association rules is proposed. Based on the ratio of the degree of support of the weak association schema in the joint database to the total record volume of the joint database, the support degree of the weak association pattern is obtained, and the frequent weak association pattern set is obtained. The improved double confidence algorithm is used to calculate the confidence of frequent and weak association pattern sets, and the weak association rules are obtained. According to the weak association rules, the classification hyperplane is calculated by the original joint database. The hyperplane is used to complete the whole classification of the joint database, the principal component analysis (PCA) method is used to reduce the dimension of the operation data in the joint database, and the operational data characteristics in the joint database are classified by the differential classification method. The effective intrusion detection of federated database under weak association rules is realized. Experiments show that the proposed method is accurate and effective.
【作者單位】: 瓊州學(xué)院電子信息工程學(xué)院;
【基金】:瓊州學(xué)院校級(jí)青年科學(xué)基金項(xiàng)目:(QYQN201338)
【分類(lèi)號(hào)】:TP311.13;TP393.08
【參考文獻(xiàn)】
相關(guān)期刊論文 前2條
1 陶樹(shù)平,屠穎;關(guān)聯(lián)規(guī)則和分類(lèi)規(guī)則挖掘算法的改進(jìn)與實(shí)現(xiàn)[J];計(jì)算機(jī)工程;2003年15期
2 張新有;曾華q,
本文編號(hào):2422719
本文鏈接:http://sikaile.net/guanlilunwen/ydhl/2422719.html
最近更新
教材專(zhuān)著