Internet Traffic Classification Using DBSCAN
Caihong Yang, Fei Wang, Benxiong Huang · 2009
In recent years, a technique based on machine learning for Internet traffic classification has attracted more and more attentions. It not only overcomes some short comings of traditional classification technique based on port number,but also does not inspect the packet payload, which involves the security and privacy. In this paper, we apply an unsupervised machine learning approach based on DBSCAN algorithm. DBSCAN algorithm has three merits: (1) minimal requirements of domain knowledge to determine the input parameters; (2) discovery of clusters with arbitrary shapes; (3)good efficiency on large data set. Experiment results show that DBSCAN has better effectiveness and efficiency.