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

摘要：

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 todays 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.

关键词： Internet aplication traffic identification traffic measurement and analysis machine learning supervised algorithm.

作者: Byungchul Park Young J.Won Mi-Jung Choi Myung-Sup Kim James W.Hong

作者单位: Dept.of Computer Science and Engineering,POSTECH,Pohang,Korea Dept.of Computer and Information Science,Korea University,Jochiwon,Korea

会议类型: 国际会议

会议名称: 11th Asia-Pacific Network Operations and Management Symposium(APNOMS 2008)(第十一届亚太网络运行和管理国际研讨会)

会议地点: 北京

会议语种:英文

页码: 474-477

在线出版日期: 2008-10-22（万方平台首次上网日期，不代表论文的发表时间）