Title :
Network traffic classification using AdaBoost Dynamic
Author :
de Souza, Erico N. ; Matwin, S. ; Fernandes, Sueli
Author_Institution :
Sch. of Inf. Technol. & Eng., Univ. of Ottawa, Ottawa, ON, Canada
Abstract :
Accurate traffic classification and identification is of paramount importance for proper network management and control in both edge and backbone networks. The use of Machine Learning (ML) algorithms has been gaining popularity due to its widespread availability and to its somewhat straightforward application to Internet traffic. This work focus on a specific case of using ML algorithms for network traffic classification. We introduce AdaBoost Dynamic with Logistic Function (AB-DL), an extension of AdaBoost. M1, that combines various classifiers to improve the final hypothesis. We carefully choose parameters from the flow records traces to improve the accuracy of the algorithms. Tests were executed with a publicly available data set from Ground Truth, and the other simulation was executed in a data set generated from University, that is not public. Results show that AB-DL achieve accuracy of 93% and 98.1%, respectively from each data set.
Keywords :
learning (artificial intelligence); pattern classification; telecommunication network management; telecommunication traffic; AB-DL; AdaBoost Dynamic with Logistic Function; AdaBoost M1; Ground Truth; Internet traffic; ML algorithms; backbone networks; edge networks; machine learning algorithms; network control; network management; network traffic classification; traffic identification; Accuracy; Decision trees; Educational institutions; Feature extraction; Heuristic algorithms; IP networks; Ports (Computers);
Conference_Titel :
Communications Workshops (ICC), 2013 IEEE International Conference on
Conference_Location :
Budapest
DOI :
10.1109/ICCW.2013.6649441