A review on machine learning–based approaches for Internet traffic classification

2020 ◽  
Vol 75 (11-12) ◽  
pp. 673-710 ◽  
Author(s):  
Ola Salman ◽  
Imad H. Elhajj ◽  
Ayman Kayssi ◽  
Ali Chehab
Keyword(s):  
Machine Learning ◽  
Internet Traffic ◽  
Traffic Classification ◽  
Internet Traffic Classification

A Method for P2P Traffic Identification Based on Semi-Supervised Learning

2014 ◽  
Vol 602-605 ◽  
pp. 1933-1937
Author(s):  
Lian Fa Wu
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Supervised Learning ◽  
Internet Traffic ◽  
Support Vector ◽  
Traffic Classification ◽  
Learning Methods ◽  
Traffic Identification ◽  
Supervised Methods ◽  
Internet Traffic Classification

In recent years, Internet traffic classification using machine learning is a hot topic, and supervised learning methods which contain Support Vector Machine were used to identify Internet traffic in many papers. The supervised learning methods need many instances which have been labeled to train classifying model, but it is difficult to label the instances because many traffic have been encrypted. Labeled instances and unlabeled instances can be used by semi-supervised learning methods to train the classifying model, so that it is very fit for p2p traffic identification. Transductive support vector machine is one of the typical semi-supervised learning methods. Based on theoretic analyzing and experiment, we compared the accuracy of TSVM and SVM. The experiment results show that the semi-supervised methods have some advantages on identification of p2p traffic.

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