Skype Traffic Identification Based SVM Using Optimized Feature Set

摘要：

Skype traffic recognition is a challenging problem due to the encryption and dynamic port number. Accuracy and timely traffic classification is critical in network security monitoring and traffic engineering. In this paper, we propose an online recognition method based on SVM (support vector machine) machine learning method. As the feature set is optimized instead of redundant, our method is able to compute faster and more accuracy. Experimental results on Collage campus data sets show that our method performs better on both speed and efficiency. Moreover, the robustness of our method is demonstrated on the other non-Skype traffic such as MSN (Microsoft Service Network), PPLive (Peer to Peer LIVE) application.

关键词： SVM Skype speed efficiency

作者: Hongli Zhang Zhimin Gu Zhenqing Tian

作者单位: School of Computer Science and Technology, Beijing Institute of Technology, Beijing 100081, China Me School of Computer Science and Technology, Beijing Institute of Technology, Beijing 100081, China Media College, Inner Mongolia Normal University, Hohhot 010022, China

会议类型: 国际会议

会议名称: 2010 International Conference on Information,Networking and Automation(2010 IEEE信息网络与自动化国际会议 ICINA 2010)

会议地点: 昆明

会议语种:英文

页码: 431-435

在线出版日期: 2010-10-17（万方平台首次上网日期，不代表论文的发表时间）