Exploiting unlabeled data to improve peer-to-peer traffic classification using incremental tri-training method-Reference-Cited by-同舟云学术

Exploiting unlabeled data to improve peer-to-peer traffic classification using incremental tri-training method

Published:2009-01-09 Issue:2 Volume:2 Page:87-97
ISSN:1936-6442
Container-title:Peer-to-Peer Networking and Applications
language:en
Short-container-title:Peer-to-Peer Netw. Appl.

Author:

Raahemi Bijan,Zhong Weicai,Liu Jing

Publisher

Springer Science and Business Media LLC

Subject

Computer Networks and Communications,Software

Link

http://link.springer.com/content/pdf/10.1007/s12083-008-0022-6.pdf

Reference25 articles.

1. Azzouna NB, Guillemin F (2004) Impact of peer-to-peer applications on wide area network traffic: an experimental approach. IEEE Global Telecommunications Conference 3:1544–1548

2. Kamei S, Kimura T (2003) Practicable network design for handling growth in the volume of peer-to-peer traffic. IEEE Pacific Rim Conference on Communications. Computers and signal Processing 2:597–600

3. Cloud Shield (2007) Peer-to-peer traffic control. (October), Available: http://www.cloudshield.com/solutions/p2pcontrol.asp

4. Zander S, Nguyen T, Armitage G (2005) Self-learning IP traffic classification based on statistical flow characteristics. Springer-Verlag Lecture Notes in Computer Science 3431:325–328 Springer Berlin

5. Zuev D, Moore AW (2005) Traffic classification using a statistical approach. Springer-Verlag Lecture Notes in Computer Science 3431:321–324 Springer Berlin

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