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Information 2018, 9(9), 233; https://doi.org/10.3390/info9090233

MODC: A Pareto-Optimal Optimization Approach for Network Traffic Classification Based on the Divide and Conquer Strategy

Informatics Center, Federal University of Pernambuco, UFPE, Recife-PE 50740-560, Brazil
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Received: 13 August 2018 / Revised: 10 September 2018 / Accepted: 11 September 2018 / Published: 13 September 2018
(This article belongs to the Section Information and Communications Technology)
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Abstract

Network traffic classification aims to identify categories of traffic or applications of network packets or flows. It is an area that continues to gain attention by researchers due to the necessity of understanding the composition of network traffics, which changes over time, to ensure the network Quality of Service (QoS). Among the different methods of network traffic classification, the payload-based one (DPI) is the most accurate, but presents some drawbacks, such as the inability of classifying encrypted data, the concerns regarding the users’ privacy, the high computational costs, and ambiguity when multiple signatures might match. For that reason, machine learning methods have been proposed to overcome these issues. This work proposes a Multi-Objective Divide and Conquer (MODC) model for network traffic classification, by combining, into a hybrid model, supervised and unsupervised machine learning algorithms, based on the divide and conquer strategy. Additionally, it is a flexible model since it allows network administrators to choose between a set of parameters (pareto-optimal solutions), led by a multi-objective optimization process, by prioritizing flow or byte accuracies. Our method achieved 94.14% of average flow accuracy for the analyzed dataset, outperforming the six DPI-based tools investigated, including two commercial ones, and other machine learning-based methods. View Full-Text
Keywords: network traffic classification; machine learning; hybrid model; multi-objective genetic algorithm; extreme learning machine; growing hierarchical self-organizing map network traffic classification; machine learning; hybrid model; multi-objective genetic algorithm; extreme learning machine; growing hierarchical self-organizing map
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
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Nascimento, Z.; Sadok, D. MODC: A Pareto-Optimal Optimization Approach for Network Traffic Classification Based on the Divide and Conquer Strategy. Information 2018, 9, 233.

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