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Volumn , Issue , 2009, Pages 60-66

Early traffic classification using Support Vector Machines

Author keywords

Support Vector Machines; Traffic classification; Traffic identification

Indexed keywords

CLASSIFICATION METHODS; COMPUTATIONAL COSTS; INTERNET TRAFFIC; LARGE NETWORKS; MODERN APPLICATIONS; NETWORK DESIGN; PRIVACY CONCERNS; REAL TIME; ROUTING OPTIMIZATION; STATISTICAL PROPERTIES; SUPERVISED MACHINE LEARNING; TRAFFIC CLASSIFICATION; TRAFFIC IDENTIFICATION;

EID: 72049125347     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1636682.1636693     Document Type: Conference Paper
Times cited : (29)

References (21)
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  • 15
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    • Internet traffic classification using bayesian analysis techniques
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    • A. W. Moore and D. Zuev. Internet traffic classification using bayesian analysis techniques. SIGMETRICS Perform. Eval. Rev., 33(1):50-60, June 2005.
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    • Moore, A.W.1    Zuev, D.2
  • 16
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    • (2009)
    • Valenti, S.1    Rossi, D.2    Meo, M.3    Mellia, M.4    Bermolen, P.5
  • 20
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    • R. D. . A. M. Wei Li, Kaysar Abdin. Approaching real-time network traffic classification. Technical Report RR-06-12, Department of Computer Science, Queen Mary, University of London, Mile End Road, London E1 4NS, UK, October 2006.
    • R. D. . A. M. Wei Li, Kaysar Abdin. Approaching real-time network traffic classification. Technical Report RR-06-12, Department of Computer Science, Queen Mary, University of London, Mile End Road, London E1 4NS, UK, October 2006.
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    • Y. xiang Yang, R. Wang, Y. Liu, S. zhen Li, and X. yong Zhou. Solving p2p traffic identification problems via optimized support vector machines. In AICCSA [21], pages 165-171.
    • Y. xiang Yang, R. Wang, Y. Liu, S. zhen Li, and X. yong Zhou. Solving p2p traffic identification problems via optimized support vector machines. In AICCSA [21], pages 165-171.


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.