DocumentCode :
3238749
Title :
A novel statistical automaton for network cloud traffic classification
Author :
Haiqiang Wang ; Kuo-Kun Tseng ; Jeng-Shyang Pan
Author_Institution :
Dept. of Comput. Sci. & Technol., Harbin Inst. of Technol., Shenzhen, China
fYear :
2012
fDate :
14-16 Aug. 2012
Firstpage :
49
Lastpage :
52
Abstract :
Traffic classification is crucial in many network and cloud applications, they are from QoS enforcement, network monitoring to security and firewalls. In recent years, all the classification with deep packet inspection (DPI) are using the exact matching with the existing policy semantics. However, if the policy semantics is changed, then the DPI classifier is no longer able to be a workable traffic classification. We proposed a new statistical automaton for the traffic classification. The applications are marked by many multiple signatures during a flow training process, and then it classifies the applications when their statistical results are reached. In the experiment, we evaluate the proposed method with 5 applications which proves our idea is feasible for the network and cloud traffic classification.
Keywords :
cloud computing; firewalls; quality of service; statistical analysis; telecommunication security; telecommunication traffic; DPI; QoS enforcement; deep packet inspection; firewalls; network cloud traffic classification; network monitoring; security; statistical automaton; Automata; Classification algorithms; Doped fiber amplifiers; Inspection; Payloads; Protocols; Training; automaton classification; deep packet inspection; statistical automaton;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Information Security and Intelligence Control (ISIC), 2012 International Conference on
Conference_Location :
Yunlin
Print_ISBN :
978-1-4673-2587-5
Type :
conf
DOI :
10.1109/ISIC.2012.6449705
Filename :
6449705
Link To Document :
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