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APNOMS
2008
Springer

Empirical Analysis of Application-Level Traffic Classification Using Supervised Machine Learning

8 years 11 months ago
Empirical Analysis of Application-Level Traffic Classification Using Supervised Machine Learning
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.
Byungchul Park, Young J. Won, Mi-Jung Choi, Myung-
Added 12 Oct 2010
Updated 12 Oct 2010
Type Conference
Year 2008
Where APNOMS
Authors Byungchul Park, Young J. Won, Mi-Jung Choi, Myung-Sup Kim, James W. Hong
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