Abstract
Accurate application traffic classification and identification are important for network monitoring and analysis. The accuracy of traditional Internet application traffic classification approaches is rapidly decreasing due to the diversity of today’s Internet application traffic, such as ephemeral port allocation, proprietary protocol, and traffic encryption. This paper presents an empirical evaluation of application-level traffic classification using supervised machine learning techniques. Our results indicate that we cannot achieve high accuracy with a simple feature set. Even if a simple feature set shows good performance in application category-level classification, more sophisticated feature selection methods and other techniques are necessary for performance enhancement.
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© 2008 Springer-Verlag Berlin Heidelberg
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Park, B., Won, Y.J., Choi, MJ., Kim, MS., Hong, J.W. (2008). Empirical Analysis of Application-Level Traffic Classification Using Supervised Machine Learning. In: Ma, Y., Choi, D., Ata, S. (eds) Challenges for Next Generation Network Operations and Service Management. APNOMS 2008. Lecture Notes in Computer Science, vol 5297. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-88623-5_55
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DOI: https://doi.org/10.1007/978-3-540-88623-5_55
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-88622-8
Online ISBN: 978-3-540-88623-5
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