發(fā)布時間：2018-06-11 13:42

  本文選題：模糊支持向量機 + 異常數(shù)據(jù)�。� 參考：《計算機仿真》2013年08期

【摘要】：網(wǎng)絡(luò)異常與普通的攻擊特征不同,沒有明顯的行為特征。尤其是大差異樣本數(shù)據(jù)集中,異常數(shù)據(jù)屬性直接差異很大,很難形成統(tǒng)一的約束規(guī)范,傳統(tǒng)的檢測算法都是假設(shè)攻擊行為特征提取的基礎(chǔ)上,對上述異常行為很難進行判斷,會出現(xiàn)判斷多中心現(xiàn)象,造成誤警率高,提出了一種大差異數(shù)據(jù)集的網(wǎng)絡(luò)異常檢測算法。針對大差異、高維度數(shù)據(jù)屬性,運用主成分分析方法,對網(wǎng)絡(luò)操作數(shù)據(jù)進行降維處理,引入一種差異行為判斷的策略,對網(wǎng)絡(luò)操作數(shù)據(jù)大差異特征進行分類處理,降低數(shù)據(jù)之間的差異性,從而保證差異行為能夠被有效的分類約束描述。實驗結(jié)果表明,利用改進算法能夠有效提高網(wǎng)絡(luò)中大差異異常數(shù)據(jù)檢測的準確性。
[Abstract]:The network anomaly is different from the common attack feature, and has no obvious behavior characteristic. Especially in the large difference sample data set, the attribute of abnormal data is very different directly, it is difficult to form the unified constraint specification. The traditional detection algorithm is based on the assumption of the feature extraction of attack behavior, so it is difficult to judge the abnormal behavior mentioned above. A network anomaly detection algorithm based on large difference data sets is proposed in this paper because of the high false alarm rate due to the phenomenon of multi-center judgment. Aiming at the attribute of large difference and high dimension data, this paper applies the principal component analysis method to reduce the dimension of network operation data, and introduces a strategy to judge the difference behavior, and classifies the large difference characteristic of network operation data. Reduce the difference between the data, so as to ensure that the differential behavior can be effectively described by the classification constraints. Experimental results show that the improved algorithm can effectively improve the accuracy of large difference anomaly data detection in the network.
【作者單位】： 佳木斯大學信息電子技術(shù)學院;
【基金】：佳木斯市重點科研課題名稱(12004) 黑龍江省教育廳科學技術(shù)研究項目(11551490)
【分類號】：TP393.08

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本文編號：2005470