[1]
Xiong Gang, Meng Jiao, Cao Zigang, et al. Research Progress and Prospects of Network Traffic Classification[J]. Journal of Integration Technology, 2012, 1(1): 32-42.
Google Scholar
[2]
Li Wei, Canini M, Moore A W. Efficient Application Identification and the Temporal and Spatial Stability of Classification Schema[J]. Computer Networks, 2009, 53(6): 790-809.
DOI: 10.1016/j.comnet.2008.11.016
Google Scholar
[3]
Sen S, Spatscheck O, Wang D. Accurate, Scalable in Network Identification of P2P Traffic Using Application Singntures[C]. In WWW2004. USA (New York), 2004: 512-521.
DOI: 10.1145/988672.988742
Google Scholar
[4]
Moore A, Papagiannaki K. Toward the accurate identification of network applications[C]. Proceedings of Passive and Active Measurement workshop. USA (Boston), 2005: 41-54.
DOI: 10.1007/978-3-540-31966-5_4
Google Scholar
[5]
Karagiannis T, Broido A, Faloutsos M, et al. Transport Layer Identification of P2P Traffic[C]. Internet Measurement Conference (IMC) 2004. USA (New York), 2004: 121-134.
DOI: 10.1145/1028788.1028804
Google Scholar
[6]
Wang Junsong, Gao Zhiwei. Network Traffic Modeling and Prediction Based on RBF Neural Network[J]. Computer Engineering and Applications, 2008, 44(13): 6-11.
Google Scholar
[7]
She Feng, Wang Xiaoling. Network Traffic Classification Based on Semi-supervised Learning[J]. Computer Engineering, 2009, 35(12): 90-94.
Google Scholar
[8]
Liang Sheng. Identification method of Internet Streaming based on SVM and clustering[J]. Computer Engineering and Design, 2010, 31(7): 1566-1569.
Google Scholar
[9]
Qiu Jing, Xia Jingbo, Bai Jun. Network Traffic Classification Using SVM Decision Tree[J]. Electrnoics Optics & Control, 2012, 19(6): 13-16.
Google Scholar
[10]
Ouyang guang, Li Qianqian, Man Junfeng. The Network Traffic Classification Techniques Based on DDAG-SVM[J]. Mathematics in Practice and Theory, 2013, 43(8): 197-203.
Google Scholar
[11]
Li Jianwu, Lu Yao. A Fast Multi-class Support Vector Machine[J]. Pattern Recognition and Artificial Inteligence, 2007, 20(3): 301-307.
Google Scholar
[12]
Yin Anrong, Xie Xiang, Kuang Jingming. Application of Hadamard ECOC in Multi-Class Problems Based on SVM[J]. ACTA ELECTRONICA SINICA, 2008, 36(1): 122-126.
DOI: 10.21437/interspeech.2005-672
Google Scholar
[13]
Cortes C, Vapnik V. Support-vector networks [J]. Machine Learning, 1995, 20: 273-297.
DOI: 10.1007/bf00994018
Google Scholar
[14]
Xu Peng, Liu Qiong, Lin Sen. Internet Traffic Classification Using Support Vector Machine[J]. Journal of Computer Research and Development, 2009, 46(3): 407-414.
Google Scholar
[15]
Ran Wang, Sam Kwong, Degang Chen. A new method for multi-class support vector machines by training least number of classifiers[C]. Proceedings of the 2011 International Conference on Machine Learning and Cybernetics. China(Guilin), 2011: 648-653.
DOI: 10.1109/icmlc.2011.6016830
Google Scholar
[16]
V N Vapnik. Statistical Learning Theory[M]. Wiley Inter-Science, 1998: 493-520.
Google Scholar
[17]
J Friedman. Another approach to polychotomous classification, Technical report [R] Stanford University, Department of Statistics, (1996).
Google Scholar
[18]
T G Dietterieh, G Bakiri. Solving Multi-class Learning Problems via Error-Correcting Output Codes[J]. Journal of Artificial Intelligence Research, 1994, 2(1): 263-286.
DOI: 10.1613/jair.105
Google Scholar
[19]
Wang Wanjiang. The Construction of Hadamard Matrix and Its Application[D]. Tianjin: Tianjin Polytechnic Uinversity, (2007).
Google Scholar