Internet Traffic Identification Using Community Detecting Algorithm

摘要：

In recent years, Internet traffic classification using machine learning has become a new direction in network measurement. Because supervised clustering algorithm need accuracy of training sets and it can not classify unknown application, we introduced complex network’s community detecting algorithm, a new unsupervised classify algorithm, which have previously not been used for network traffic classification. We evaluate this algorithm and compare it to the previously used unsupervised K-means and DBSCAN algorithm, using empirical Internet traces. The experiment results show complex network’s community detecting algorithm work very well in accuracy and produces better clusters, besides, complex network’s community detecting algorithm need not know the number of the traffic application beforehand.

关键词： traffic classification complex network community detection algorithm flow network measurement

作者: CAI Jun YU Shun-Zheng

作者单位: Department of Electronic and Communication Engineering Sun Yat-Sen University, Guangzhou 510275, P. Department of Electronic and Communication Engineering Sun Yat-Sen University, Guangzhou 510275, P.

会议类型: 国际会议

会议名称: 2010年IEEE多媒体信息网络与安全国际会议

会议地点: 南京

会议语种:英文

页码: 164-168

在线出版日期: 2010-11-01（万方平台首次上网日期，不代表论文的发表时间）