SmoteAdaNL: a learning method for network traffic classification-Reference-Cited by-同舟云学术

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SmoteAdaNL: a learning method for network traffic classification

Published:2015-08-05 Issue:1 Volume:7 Page:121-130
ISSN:1868-5137
Container-title:Journal of Ambient Intelligence and Humanized Computing
language:en
Short-container-title:J Ambient Intell Human Comput

Author:

Liu Zhen,Wang Ruoyu,Tao Ming

Funder

National Natural Science Fund

Guangdong Province Natural Science Foundation

Guangdong Higher School Scientic Innovation Project

Fundamental Research Funds for the Central Universities

China Postdoctoral Science Foundation

Publisher

Springer Science and Business Media LLC

Subject

General Computer Science

Link

http://link.springer.com/content/pdf/10.1007/s12652-015-0310-y.pdf

Reference27 articles.

1. Carela-Español V, Barlet-Ros P, Cabellos-Aparicio A, Solé-Pareta J (2010) Analysis of the impact of sampling on netflow traffic classification. Comput Netw 55(5):1083–1099. doi: 10.1016/j.comnet.2010.11.002 (ISSN:1389-1286)

2. Chawla NV, Bowyer KW, Hall LO, Kegelmeyer WP (2002) Smote: synthetic minority over-sampling technique. J Artif Intell Res 16(1):321–357 (ISSN:1076-9757)

3. Dainotti A, Pescapé A (2012) Issues and future directions in traffic classification. IEEE Netw 26(1):35–40. ISSN:0890-8044. doi: 10.1109/MNET.2012.6135854

4. Dewaele G, Himura Y, Borgnat P, Fukuda K, Abry P, Michel O, Fontugne R, Cho K, Esaki H (2010) Unsupervised host behavior classification from connection patterns. Int J Netw Manag 20(5):317–337. doi: 10.1002/nem.750

5. Erman J, Mahanti A, Arlitt M, Cohen I, Williamson C (2007a) Offline/realtime traffic classification using semi-supervised learning. Perform Eval 64(9–12):1194–1213. doi: 10.1016/j.peva.2007.06.014 (ISSN:0166-5316)

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