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Improved Network Traffic Classification Using Ensemble Learning

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSNConference contribution/Paperpeer-review

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Improved Network Traffic Classification Using Ensemble Learning. / Possebon, Isadora; da Silva, Anderson; Granville, Lisandro; Schaeffer-Filho, Alberto ; Marnerides, Angelos.

IEEE Symposium on Computers and Communications (ISCC) 2019. IEEE, 2020. p. 1-6.

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSNConference contribution/Paperpeer-review

Harvard

Possebon, I, da Silva, A, Granville, L, Schaeffer-Filho, A & Marnerides, A 2020, Improved Network Traffic Classification Using Ensemble Learning. in IEEE Symposium on Computers and Communications (ISCC) 2019. IEEE, pp. 1-6, IEEE Symposium on Computers and Communications (ISCC) 2019, Barcelona, Spain, 29/06/19. https://doi.org/10.1109/ISCC47284.2019.8969637

APA

Possebon, I., da Silva, A., Granville, L., Schaeffer-Filho, A., & Marnerides, A. (2020). Improved Network Traffic Classification Using Ensemble Learning. In IEEE Symposium on Computers and Communications (ISCC) 2019 (pp. 1-6). IEEE. https://doi.org/10.1109/ISCC47284.2019.8969637

Vancouver

Possebon I, da Silva A, Granville L, Schaeffer-Filho A, Marnerides A. Improved Network Traffic Classification Using Ensemble Learning. In IEEE Symposium on Computers and Communications (ISCC) 2019. IEEE. 2020. p. 1-6 https://doi.org/10.1109/ISCC47284.2019.8969637

Author

Possebon, Isadora ; da Silva, Anderson ; Granville, Lisandro ; Schaeffer-Filho, Alberto ; Marnerides, Angelos. / Improved Network Traffic Classification Using Ensemble Learning. IEEE Symposium on Computers and Communications (ISCC) 2019. IEEE, 2020. pp. 1-6

Bibtex

@inproceedings{6b961f8a1ab6484db5a62bc059a6083b,
title = "Improved Network Traffic Classification Using Ensemble Learning",
abstract = "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%.",
author = "Isadora Possebon and {da Silva}, Anderson and Lisandro Granville and Alberto Schaeffer-Filho and Angelos Marnerides",
note = "{\textcopyright}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.; IEEE Symposium on Computers and Communications (ISCC) 2019 : 24th IEEE Symposium on Computers and Communications (ISCC 2019), IEEE ISCC 2019 ; Conference date: 29-06-2019 Through 03-07-2019",
year = "2020",
month = jan,
day = "27",
doi = "10.1109/ISCC47284.2019.8969637",
language = "English",
pages = "1--6",
booktitle = "IEEE Symposium on Computers and Communications (ISCC) 2019",
publisher = "IEEE",
url = "http://paradise.site.uottawa.ca/iscc2019/",

}

RIS

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 -