Title :
A novel P2P traffic identification model based on machine learning
Author :
Xu, He ; Wang, Suoping ; Wang, Ruchuan
Author_Institution :
College of Computer, Nanjing University of Posts and Telecommunications, China
Abstract :
P2P traffic identification model based on machine learning is proposed. The FCBF(Fast Correlation-Based Filter) feature selection algorithm is used to select the P2P flow attribute features subset. A P2P flows identification model is built based on decision tree and FCBF. 10-fold cross-validation method is used to validate the proposed model. Experimental results show that the method of P2P traffic identification based on decision tree is feasible and the FCBF method is a useful method for extracting features from P2P flows.
Keywords :
Artificial neural networks; Computational modeling; Computers; Decision trees; Feature extraction; Machine learning; Servers; P2P; decision tree; feature selection; flow identification; machine learning;
Conference_Titel :
Information Science and Engineering (ICISE), 2010 2nd International Conference on
Conference_Location :
Hangzhou, China
Print_ISBN :
978-1-4244-7616-9
DOI :
10.1109/ICISE.2010.5691674
