Internet Traffic Classification Using Constrained Clustering

Provided by: Institute of Electrical & Electronic Engineers
Topic: Big Data
Format: PDF
Statistics-based Internet traffic classification using machine learning techniques has attracted extensive research interests lately, because of the increasing ineffectiveness of traditional port-based and payload-based approaches. In particular, unsupervised learning, i.e. traffic clustering, is very important in real-life applications, where labeled training data are difficult to obtain and new patterns keep emerging. Although previous studies have applied some classic clustering algorithms such as K-Means and EM for the task, the quality of resultant traffic clusters was far from satisfactory.

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