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| Machine Learning Based Network Traffic Classification: A Survey |
| Bin Hu, Yi Shen |
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Abstract The classification of network applications is essential to numerous
network activities, including quality of service, accounting, and
intrusion detection. Originally, port-match was regarded as the
most popular and effective method. After that, with the booming of
new Internet businesses, researchers shifted to the use of payload
analysis. However, the payload analysis is unable to process
encrypted traffic, which motivated scientists to develop more
general and effective solutions. To do so, traffic classification
has been of great concern to academia and industry and gradually
formed a relatively independent area of research. Due to the ability
of handling large number of flow samples and multidimensional
feature spaces efficiently, Machine Learning based classification
has stood out among multitude research findings. This paper aims to
provide an overview of recent advances in such study area. We focus
on how to divide machine learning based classification into two
categories: supervised and clustering. We also present the
algorithms of creating specific feature sets and classification
models such as Genetic Algorithm and Bayesian algorithm. Finally,
we compare the efficiency of these algorithms and discuss the future
direction of machine learning based classification.
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