Timely traffic identification on P2P streaming media

doi:10.1016/S1005-8885(11)60247-1

中国邮电高校学报(英文) ›› 2012, Vol. 19 ›› Issue (2): 67-73.doi: 10.1016/S1005-8885(11)60247-1

Timely traffic identification on P2P streaming media

杨洁,袁仑,贺阳,CHEN Lu-ying

北京邮电大学

收稿日期:2011-09-05 修回日期:2011-12-22 出版日期:2012-04-30 发布日期:2012-04-17
通讯作者: 袁仑 E-mail:yuan_lun@bupt.edu.cn
基金资助:
This work was supported by the National Natural Science Foundation of China (61072061), the National Science and Technology Major Project (2012ZX03002008003), the 111 Project of China (B08004), and the Chinese Universities Scientific Fund (2011RC0116).

Timely traffic identification on P2P streaming media

Beijing Key Laboratory of Network System Architecture and Convergence, School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China

Received:2011-09-05 Revised:2011-12-22 Online:2012-04-30 Published:2012-04-17
Contact: Lun YUAN E-mail:yuan_lun@bupt.edu.cn
Supported by:
This work was supported by the National Natural Science Foundation of China (61072061), the National Science and Technology Major Project (2012ZX03002008003), the 111 Project of China (B08004), and the Chinese Universities Scientific Fund (2011RC0116).

摘要/Abstract

摘要：

Since the year of 2006, peer-to-peer (P2P) streaming media service has been developing rapidly, the user scale and income scale achieve synchronous growth. However, while people enjoying the benefits of the distributed resources, a great deal of network bandwidth is consumed at the same time. Research on P2P streaming traffic characteristics and identification is essential to Internet service providers (ISPs) in terms of network planning and resource allocation. In this paper, we introduce the current common P2P traffic detection technology, and analyze the payload length distribution and payload length pattern in one flow of four popular P2P streaming media applications. Combining with the deep flow inspection and machine learning algorithm, a nearly real-time identification approach for P2P streaming media is proposed. The experiments proved that this approach can achieve a high accuracy with low false positives.

关键词:

deep flow inspection, machine learning, payload length distribution, traffic identification

Abstract:

Key words:

deep flow inspection, machine learning, payload length distribution, traffic identification

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Timely traffic identification on P2P streaming media

Timely traffic identification on P2P streaming media

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