Title: Multi-model coupling method for imbalanced network traffic classification based on clustering

Authors: Zhengzhi Tang; Xuewen Zeng; Jun Chen

Addresses: National Network New Media Engineering Research Center, Institute of Acoustics, Chinese Academy of Sciences (IACAS), Beijing, China; University of Chinese Academy of Sciences, Beijing – 100190, China ' National Network New Media Engineering Research Center, Institute of Acoustics, Chinese Academy of Sciences (IACAS), Beijing, China; University of Chinese Academy of Sciences, Beijing – 100190, China ' National Network New Media Engineering Research Center, Institute of Acoustics, Chinese Academy of Sciences (IACAS), Beijing, China; University of Chinese Academy of Sciences, Beijing – 100190, China

Abstract: The imbalanced category of network traffic poses a challenge to the classification methods based on machine learning, because the unbalanced data structure affects the performance of machine learning algorithms. In this paper, we propose a multi-model coupling approach to address the imbalanced data problem in network traffic classification. We process the major class to some clusters by a clustering algorithm. Then, these clusters and the minor class are used to form the training dataset for training model respectively. During the test, the test dataset is input into the previously trained models respectively, and the classification results of respective models are coupled to obtain the final result. We tested our proposed method on two well-known network traffic datasets and the results showed that it could achieve better performance and less time consumption compared with recent proposed methods in the case where the ratio of minor to major classes is very small.

Keywords: machine learning; imbalanced network traffic classification; clustering algorithm; multi-model coupling.

DOI: 10.1504/IJHPCN.2020.110252

International Journal of High Performance Computing and Networking, 2020 Vol.16 No.1, pp.26 - 35

Received: 23 Apr 2019
Accepted: 26 Oct 2019

Published online: 28 Sep 2020 *

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