Title :
Study of Double-Characteristics-Based SVM Method for P2P Traffic Identification
Author :
Chen, Hongwei ; Zhou, Xin ; You, Fangping ; Wang, Chunzhi
Author_Institution :
Coll. of Comput. Sci. & Technol., Huazhong Univ. of Sci. & Technol., Wuhan, China
Abstract :
In this paper, an SVM (Support Vector Machines)-based P2P (Peer-to-peer) traffic identification algorithm is presented. It could capture traffic information online, training-offline and categories online. The SVM algorithm uses double characteristics IP and IP-Port to identify P2P traffic by means of different traffic features separately. From results of experiments, we proved that choosing the appropriate traffic features, kernel option, configuration parameters, and punish modulus to the RBF kernel function of SVM algorithm are effective to identify P2P traffic.
Keywords :
peer-to-peer computing; radial basis function networks; support vector machines; telecommunication traffic; P2P traffic identification; RBF kernel function; double-characteristics-based SVM method; peer-to-peer traffic identification algorithm; support vector machines; Support vector machines; Deep Flow Inspection; Peer-to-Peer; Support Vector Machines; Traffic Identification;
Conference_Titel :
Networks Security Wireless Communications and Trusted Computing (NSWCTC), 2010 Second International Conference on
Conference_Location :
Wuhan, Hubei
Print_ISBN :
978-0-7695-4011-5
Electronic_ISBN :
978-1-4244-6598-9
DOI :
10.1109/NSWCTC.2010.54
