發(fā)布時間：2019-05-19 06:52

【摘要】：為精確識別具體的攻擊行為,提高入侵行為的識別率,提出一種基于PCA-2KPCA-SVM的pod入侵高精度檢測方法。根據(jù)樣本的不同分布特點選擇不同的PCA方法,進行特征抽取和降維預處理;對于所有入侵行為樣本,將pod入侵樣本同其它入侵行為樣本及正常樣本做一對一分組;所有小組均使用PCA-PSO-SVM方法訓練,將訓練效果不佳的小組使用2KPCA-SVM方法訓練,根據(jù)每個小組的訓練方法對測試樣本進行檢測。實驗結果表明,該方法可以精確識別pod入侵行為,可推廣到對其它入侵行為的高精度檢測。
[Abstract]:In order to accurately identify specific attack behavior and improve the recognition rate of intrusion behavior, a high precision detection method of pod intrusion based on PCA-2KPCA-SVM is proposed. According to the different distribution characteristics of the samples, different PCA methods are selected for feature extraction and dimension reduction preprocessing, and for all intrusion samples, the pod intrusion samples are grouped one-to-one with other intrusion samples and normal samples. All groups were trained by PCA-PSO-SVM method, and the groups with poor training effect were trained by 2KPCA-SVM method, and the test samples were tested according to the training methods of each group. The experimental results show that this method can accurately identify pod intrusion behavior and can be extended to high precision detection of other intrusion behaviors.
【作者單位】： 無錫職業(yè)技術學院物聯(lián)網(wǎng)技術學院;江南大學物聯(lián)網(wǎng)工程學院;
【基金】：江蘇省產(chǎn)學研聯(lián)創(chuàng)基金項目(BY2013015-40)
【分類號】：TP393.08
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本文編號：2480491