With rapid growth of internet traffic over last few years, the area
of internet traffic classification becomes very significant for
various ISPs. Now days, traditional internet traffic classification
techniques such as port number and payload based techniques are
seldom used because of use of dynamic port number instead of
well-known port number in packet headers and various cryptographic
techniques used to encrypt packet payload. Current trends are use
of machine learning techniques for internet traffic classification.
In this research work, downloaded internet traffic dataset,
self-developed internet traffic datasets for packet capture
duration of 2 minute and 2 seconds and reduced feature datasets
developed using Correlation based Feature Selection Algorithm are
employed for analysis purpose. Then, five ML algorithms Multilayer
Perceptron, Radial Basis Function Neural Network, C4.5 Decision
Tree, Bayes Net and Naive Bayes algorithms are used for internet
traffic classification. This analysis shows that C4.5 is an
effective ML technique for internet traffic classification provided
packet capture duration and number of features characterizing each
sample should be minimum."
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