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2010

An SVM-based machine learning method for accurate internet traffic classification

8 years 7 months ago
An SVM-based machine learning method for accurate internet traffic classification
Accurate and timely traffic classification is critical in network security monitoring and traffic engineering. Traditional methods based on port numbers and protocols have proven to be ineffective in terms of dynamic port allocation and packet encapsulation. The signature matching methods, on the other hand, require a known signature set and processing of packet payload, can only handle the signatures of a limited number of IP packets in real-time. A machine learning method based on SVM (supporting vector machine) is proposed in this paper for accurate Internet traffic classification. The method classifies the Internet traffic into broad application categories according to the network flow parameters obtained from the packet headers. An optimized feature set is obtained via multiple classifier selection methods. Experimental results using traffic from campus backbone show that an accuracy of 99.42% is achieved with the regular biased training and testing samples. An accuracy of 97.17% ...
Ruixi Yuan, Zhu Li, Xiaohong Guan, Li Xu
Added 05 Mar 2011
Updated 05 Mar 2011
Type Journal
Year 2010
Where ISF
Authors Ruixi Yuan, Zhu Li, Xiaohong Guan, Li Xu
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