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Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
}
TY - GEN
T1 - Improved Network Traffic Classification Using Ensemble Learning
AU - Possebon, Isadora
AU - da Silva, Anderson
AU - Granville, Lisandro
AU - Schaeffer-Filho, Alberto
AU - Marnerides, Angelos
N1 - ©2020 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
PY - 2020/1/27
Y1 - 2020/1/27
N2 - 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%.
AB - 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%.
U2 - 10.1109/ISCC47284.2019.8969637
DO - 10.1109/ISCC47284.2019.8969637
M3 - Conference contribution/Paper
SP - 1
EP - 6
BT - IEEE Symposium on Computers and Communications (ISCC) 2019
PB - IEEE
T2 - IEEE Symposium on Computers and Communications (ISCC) 2019
Y2 - 29 June 2019 through 3 July 2019
ER -