基于集成学习的软件定义网络流量分类

Won-Ju Eom, Yeong-Jun Song, Chang-hoon Park, Jeong-Keun Kim, Geon-Hwan Kim, You-Ze Cho
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引用次数: 5

摘要

准确的网络流分类对网络管理至关重要。但是,现有的网络流分类方法在分类性能、用户隐私性、时延、控制开销等方面都不能满足实际网络的需求。因此,基于机器学习的方法已被用于网络流量分类。本文提出了一种基于软件定义网络(SDN)架构的网络流量分类框架。所提出的框架完全位于网络控制器中;因此,我们可以利用SDN控制器优越的计算能力、全局可见性和可编程性来实现实时、自适应和准确的流量分类。我们还应用了四种集成算法,并从正确率、精密度、召回率、f1分数、训练时间和分类时间等方面分析了它们的分类性能。实验结果表明,基于集成模型的网络流量分类器优于基于所提框架和真实网络流量数据集的其他分类器。值得注意的是,LightGBM模型的分类性能最好。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Network Traffic Classification Using Ensemble Learning in Software-Defined Networks
Accurate network traffic classification is essential for network management. However, existing network traffic classification methods cannot meet the demand of real networks in terms of classification performance, user privacy, latency, and control overhead. Thus, a machine learning-based approach has been used for network traffic classification. In this paper, we propose a network traffic classification framework using software-defined network (SDN) architecture. The proposed framework is entirely located in the network controller; thus, we can leverage the superior computational capacity, global visibility, and programmability of the SDN controller to realize real-time, adaptive, and accurate traffic classification. We also apply four ensemble algorithms and analyze their classification performance in terms of accuracy, precision, recall, F1-score, training time, and classification time. The experimental results reveal that ensemble model-based network traffic classifiers outperform other classifiers based on the proposed framework and the real-world network traffic dataset. Notably, the LightGBM model achieves the best classification performance.
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