【摘要】：聯(lián)合數據庫的入侵和普通入侵不同,其無顯著的行為特征,入侵數據屬性差異較大,很難形成統(tǒng)一的約束規(guī)范,導致傳統(tǒng)的入侵檢測方法,由于通過提取入侵行為特征進行入侵檢測,無法有效且準確地完成聯(lián)合數據庫的入侵檢測,提出一種弱關聯(lián)規(guī)則下的聯(lián)合數據庫入侵檢測方法,通過弱關聯(lián)模式在聯(lián)合數據庫中支持程度與聯(lián)合數據庫記錄總量的比求出弱關聯(lián)模式的支持度,獲取頻繁弱關聯(lián)模式集,采用改進的雙置信度算法對頻繁弱關聯(lián)模式集的置信度進行計算,獲取弱關聯(lián)規(guī)則,依據弱關聯(lián)規(guī)則,通過原始聯(lián)合數據庫對分類超平面進行計算,采用該超平面完成聯(lián)合數據庫的整體分類,采用主成分分析方法對聯(lián)合數據庫中的操作數據進行降維處理,通過差異分類方法,對聯(lián)合數據庫中的操作數據特征進行分類操作,實現(xiàn)弱關聯(lián)規(guī)則下聯(lián)合數據庫的有效入侵檢測。實驗表明,所提方法具有很高的準確性及有效性。
[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】： 瓊州學院電子信息工程學院;
【基金】：瓊州學院校級青年科學基金項目:(QYQN201338)
【分類號】：TP311.13;TP393.08

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本文編號：2422719