Title :
Applying multiple time series data mining to large-scale network traffic analysis
Author :
He, Weisong ; Hu, Guangmin ; Yao, Xingmiao ; Kan, Guangyuan ; Wang, Hong ; Xiang, Hongmei
Author_Institution :
Sch. of Commun. & Inf. Eng., Univ. of Electron. Sci. & Technol. of China, Chengdu
Abstract :
Minimize false positive and false negative is one of the difficult problems of network traffic analysis. This paper propose a large-scale communications network traffic feature analysis method using multiple time series data mining, analyze multiple traffic feature time series as a whole, produce valid association rules of abnormal network traffic feature, characterize the entire communication network security situation accurately. Experiment with Abilene network data verify this method.
Keywords :
data mining; principal component analysis; telecommunication computing; telecommunication security; telecommunication traffic; time series; time-frequency analysis; Abilene network; association rules; communication network security; large-scale communications network; large-scale network traffic feature analysis; multiple time series data mining; principal components analysis; Communication networks; Data mining; Data security; Information analysis; Large-scale systems; Pattern analysis; Performance analysis; Telecommunication traffic; Time series analysis; Traffic control; multiple time series data mining; network traffic analysis; principal components analysis; symbolic time series analysis; time frequency analysis;
Conference_Titel :
Cybernetics and Intelligent Systems, 2008 IEEE Conference on
Conference_Location :
Chengdu
Print_ISBN :
978-1-4244-1673-8
Electronic_ISBN :
978-1-4244-1674-5
DOI :
10.1109/ICCIS.2008.4670844
