Improved Network Traffic Classification Using Ensemble Learning

Possebon, I. P., Silva, A. S., Granville, L. Z., Schaeffer-Filho, A. and Marnerides, A. (2019) Improved Network Traffic Classification Using Ensemble Learning. In: 2019 IEEE Symposium on Computers and Communications (ISCC), Barcelona, Spain, 29 Jun - 03 Jul 2019, ISBN 9781728129990 (doi: 10.1109/ISCC47284.2019.8969637)

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Despite the large number of research efforts that applied specific machine learning algorithms for network traffic classification, recent work has highlighted limitations and particularities of individual algorithms that make them more suitable to specific types of traffic and scenarios. As such, an important topic in this area is how to combine individual algorithms using meta-learning techniques in order to obtain more robust traffic classification metrics. This paper presents a comparative analysis among meta-learning approaches and individual classifiers to classify network traffic. We investigate and evaluate a range of meta-learning techniques, including Voting, Stacking, Bagging and Boosting. We then propose a new experimental analysis of different meta-learning techniques - also known as ensemble learners- and compare them with their own base classifiers when used individually. Finally, considering the emerging popularity of Neural Networks, we analyze this scenario using the Multi-layer Perceptron classifier. The experiments were performed with data provided by the UCI Machine Learning Repository. The best performance was obtained by an ensemble technique (Bagging), which obtained accuracy of 99.972% and false positive rate of 0.00018%.

Item Type:Conference Proceedings
Glasgow Author(s) Enlighten ID:Marnerides, Dr Angelos
Authors: Possebon, I. P., Silva, A. S., Granville, L. Z., Schaeffer-Filho, A., and Marnerides, A.
College/School:College of Science and Engineering > School of Computing Science
Published Online:27 January 2020

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