Flow traffic classification with support vector machine by using payload length

Masayoshi Kohara; Yoshiaki Hori; Kouichi Sakurai; Heejo Lee; Jae Cheol Ryou

doi:10.1109/CSA.2009.5404180

Flow traffic classification with support vector machine by using payload length

Masayoshi Kohara, Yoshiaki Hori, Kouichi Sakurai, Heejo Lee, Jae Cheol Ryou

Mathematical Informatics

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

1 Citation (Scopus)

Abstract

Classifying traffic is an important task for effective network planning and design, and monitoring the trends of the applications in operational networks. In this paper, we propose flow traffic classification methods using support vector machine. Classifying traffic is an important task for effective network planning and design, and monitoring the trends of the applications in operational networks. The proposals satisfy the following three requirements. Using to only flow information, not using port numbers, automatic making of traffic models. In this paper, we provide an empirical evaluation of our proposals using datasets of MIT Lincoln Laboratory, which illustrates that our proposals can classify network traffic flow over 90 % precision.

Original language	English
Title of host publication	Proceedings of the 2009 2nd International Conference on Computer Science and Its Applications, CSA 2009
DOIs	https://doi.org/10.1109/CSA.2009.5404180
Publication status	Published - Dec 1 2009
Event	2009 2nd International Conference on Computer Science and Its Applications, CSA 2009 - Jeju Island, Korea, Republic of Duration: Dec 10 2009 → Dec 12 2009

Other

Other	2009 2nd International Conference on Computer Science and Its Applications, CSA 2009
Country/Territory	Korea, Republic of
City	Jeju Island
Period	12/10/09 → 12/12/09

All Science Journal Classification (ASJC) codes

Computational Theory and Mathematics
Computer Science Applications

Access to Document

10.1109/CSA.2009.5404180

Cite this

Flow traffic classification with support vector machine by using payload length. / Kohara, Masayoshi; Hori, Yoshiaki; Sakurai, Kouichi; Lee, Heejo; Ryou, Jae Cheol.

Proceedings of the 2009 2nd International Conference on Computer Science and Its Applications, CSA 2009. 2009. 5404180.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Kohara, M, Hori, Y, Sakurai, K, Lee, H & Ryou, JC 2009, Flow traffic classification with support vector machine by using payload length. in Proceedings of the 2009 2nd International Conference on Computer Science and Its Applications, CSA 2009., 5404180, 2009 2nd International Conference on Computer Science and Its Applications, CSA 2009, Jeju Island, Korea, Republic of, 12/10/09. https://doi.org/10.1109/CSA.2009.5404180

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author = "Masayoshi Kohara and Yoshiaki Hori and Kouichi Sakurai and Heejo Lee and Ryou, {Jae Cheol}",

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AU - Hori, Yoshiaki

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PY - 2009/12/1

Y1 - 2009/12/1

N2 - Classifying traffic is an important task for effective network planning and design, and monitoring the trends of the applications in operational networks. In this paper, we propose flow traffic classification methods using support vector machine. Classifying traffic is an important task for effective network planning and design, and monitoring the trends of the applications in operational networks. The proposals satisfy the following three requirements. Using to only flow information, not using port numbers, automatic making of traffic models. In this paper, we provide an empirical evaluation of our proposals using datasets of MIT Lincoln Laboratory, which illustrates that our proposals can classify network traffic flow over 90 % precision.

AB - Classifying traffic is an important task for effective network planning and design, and monitoring the trends of the applications in operational networks. In this paper, we propose flow traffic classification methods using support vector machine. Classifying traffic is an important task for effective network planning and design, and monitoring the trends of the applications in operational networks. The proposals satisfy the following three requirements. Using to only flow information, not using port numbers, automatic making of traffic models. In this paper, we provide an empirical evaluation of our proposals using datasets of MIT Lincoln Laboratory, which illustrates that our proposals can classify network traffic flow over 90 % precision.

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Flow traffic classification with support vector machine by using payload length

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