DocumentCode :
686405
Title :
Incremental principal component analysis method on online network anomaly detection
Author :
Li Bai-nan ; Yao Dong ; Qian Ye-kui
Author_Institution :
Nat. Digital Switching Syst. Eng. & Technol. Res. Center, Zhengzhou, China
fYear :
2013
fDate :
22-24 Nov. 2013
Firstpage :
1
Lastpage :
6
Abstract :
Although PCA (principal component analysis) based multivariate anomaly detection algorithm can perform detection task, it cannot satisfy the needs of online detection due to the time complexity. To conquer this limitation, a multivariate online anomaly detection algorithm based on incremental PCA (IPCA) was proposed. The algorithm constructed normal model of traffic matrix incrementally and implemented online detection with this model. Analysis with Internet real traffic data and simulation data shows that this algorithm can perform online anomaly detection effectively.
Keywords :
computational complexity; matrix algebra; principal component analysis; security of data; Internet real traffic data; Internet simulation data; PCA based multivariate anomaly detection algorithm; incremental PCA; incremental principal component analysis method; online network anomaly detection; time complexity; traffic matrix; incremental model; network anomaly detection; singular value decomposition(SVD);
fLanguage :
English
Publisher :
iet
Conference_Titel :
Information and Network Security (ICINS 2013), 2013 International Conference on
Conference_Location :
Beijing
Electronic_ISBN :
978-1-84919-729-8
Type :
conf
DOI :
10.1049/cp.2013.2472
Filename :
6826021
Link To Document :
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