Network Traffic Classification Based on Message Statistics

• Published in: 2008 4th International Conference on Wireless Communications, Networking and Mobile Computing pp. 1 - 4
• Main Authors: Gang Shen, Lian Fan
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.10.2008
• Subjects: Application software; Classification tree analysis; Decision trees; Engineering management; Machine learning algorithms; Payloads; Protocols; Software engineering; Statistics; Telecommunication traffic
• ISBN: 9781424421077; 1424421071
• ISSN: 2161-9646
• DOI: 10.1109/WiCom.2008.1046

Accurate and efficient network traffic classification is an important network management task. Two way messages in a session follow the underlying application protocol to exchange information. In this paper, we propose a novel application classification method based on message statistics, concisely representing the protocols' unique characteristics. We present algorithms using SVD-based and information gain based algorithms to select the proper message feature set. As shown by the evaluation experiments, using the selected message features, a simple decision tree is able to reach the classification accuracy over 99%, which is comparable to other more sophisticated machine learning results.