Internet Traffic Identification Using Community Detecting Algorithm
Jun Cai, Shun‐Zheng Yu · 2010
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.