%0 Journal Article
%T Internet Traffic Classification Based on Detecting Community Structure in Complex Network<br>基于复杂网络社团划分的网络流量分类
%A CAI Jun
%A YU Shun-zheng
%A <br>蔡君
%A 余顺争
%J 计算机科学
%D 2011
%I 
%X In recent years, Internet traffic classification using port based or payload-based methods is becoming increasingly difficult with peer-to-peer(P2P) applications using dynamic port numbers,masquerading techniques, and encryption to avoid detection. Because supervised clustering algorithm needs accuracy of training sets and it can not classify unknown apphcation,we introduced complex network's community detecting algorithm,a new unsupervised classify algorithm, which has previously not been used for network traffic classification. We evaluated this algorithm and compared it with the previously used unsupervised K-means and DBSCAN algorithm, using empirical Internet traces. The experiment results show complex network's community detecting algorithm works 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.
%K Traffic classification
%K Unsupervised clustering
%K Community detecting algorithm
%K Complex network<br>流量分类，无监督聚类，社团划分，复杂网络
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=64A12D73428C8B8DBFB978D04DFEB3C1&aid=9ABCDF022A81B65E370BD78BDF3EDBFB&yid=9377ED8094509821&vid=16D8618C6164A3ED&iid=38B194292C032A66&sid=E203FB1A272C9DD2&eid=0D0D661F0B316AD5&journal_id=1002-137X&journal_name=计算机科学&referenced_num=0&reference_num=15