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

Authors:
Byungchul Park;Young J. Won;Mi-Jung Choi;Myung-Sup Kim;James W. Hong
Affiliations:
Dept. of Computer Science and Engineering, POSTECH, Pohang, Korea;Dept. of Computer Science and Engineering, POSTECH, Pohang, Korea;Dept. of Computer Science and Engineering, POSTECH, Pohang, Korea;Dept. of Computer and Information Science, Korea University, Jochiwon, Korea;Dept. of Computer Science and Engineering, POSTECH, Pohang, Korea
Venue:
APNOMS '08 Proceedings of the 11th Asia-Pacific Symposium on Network Operations and Management: Challenges for Next Generation Network Operations and Service Management
Year:
2008

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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.