Abstract
Accurate traffic classification is the keystone of numerous network activities. Our work capitalises on hand-classified network data, used as input to a supervised Bayes estimator. We illustrate the high level of accuracy achieved with a supervised Naïve Bayes estimator; with the simplest estimator we are able to achieve better than 83% accuracy on both a per-byte and a per-packet basis.
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© 2005 Springer-Verlag Berlin Heidelberg
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Zuev, D., Moore, A.W. (2005). Traffic Classification Using a Statistical Approach. In: Dovrolis, C. (eds) Passive and Active Network Measurement. PAM 2005. Lecture Notes in Computer Science, vol 3431. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-31966-5_25
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DOI: https://doi.org/10.1007/978-3-540-31966-5_25
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-25520-8
Online ISBN: 978-3-540-31966-5
eBook Packages: Computer ScienceComputer Science (R0)