An early recognition algorithm for BitTorrent traffic based on improved K-means

被引：2

作者：

Rong Hui-gui ^{[1
]}

Li Ming-wei ^{[1
]}

Cai Li-jun ^{[1
]}

机构：

[1] Hunan Univ, Sch Informat Sci & Engn, Changsha 410082, Hunan, Peoples R China

来源：

JOURNAL OF CENTRAL SOUTH UNIVERSITY OF TECHNOLOGY | 2011年 / 18卷 / 06期

基金：

中国国家自然科学基金;

关键词：

traffic identification; early recognition algorithm; cluster radius; false positive/negative rate;

D O I：

10.1007/s11771-011-0943-8

中图分类号：

TF [冶金工业];

学科分类号：

0806 ;

摘要：

In response to the deficiencies of BitTorrent, the concept of density radius was proposed, and the distance from the maximum point of radius density to cluster center as a cluster radius was taken to solve the too large cluster radius resulted from the discrete points and to reduce the false positive rate of early recognition algorithms. Simulation results show that in the actual network environment, the improved algorithm, compared with K-means, will reduce the false positive rate of early identification algorithm from 6.3% to 0.9% and has a higher operational efficiency.

引用

页码：2061 / 2067

页数：7

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