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Combined oversampling and undersampling method based on slow-start algorithm for imbalanced network traffic

Authors:

Seunghyun Park,

Hyunhee ParkAuthors Info & Claims

Computing, Volume 103, Issue 3

Pages 401 - 424

https://doi.org/10.1007/s00607-020-00854-1

Published: 01 March 2021 Publication History

Abstract

Network traffic data basically comprise a major amount of normal traffic data and a minor amount of attack data. Such an imbalance problem in the amounts of the two types of data reduces prediction performance, such as by prediction bias of the minority data and miscalculation of normal data as outliers. To address the imbalance problem, representative sampling methods include various minority data synthesis models based on oversampling. However, as the oversampling method for resolving the imbalance problem involves repeatedly learning the same data, the classification model can overfit the learning data. Meanwhile, the undersampling methods proposed to address the imbalance problem can cause information loss because they remove data. To improve the performance of these oversampling and undersampling approaches, we propose an oversampling ensemble method based on the slow-start algorithm. The proposed combined oversampling and undersampling method based on the slow-start (COUSS) algorithm is based on the congestion control algorithm of the transmission control protocol. Therefore, an imbalanced dataset oversamples until overfitting occurs, based on a minimally applied undersampling dataset. The simulation results obtained using the KDD99 dataset show that the proposed COUSS method improves the F1 score by 8.639%, 6.858%, 5.003%, and 4.074% compared to synthetic minority oversampling technique (SMOTE), borderline-SMOTE, adaptive synthetic sampling, and generative adversarial network oversampling algorithms, respectively. Therefore, the COUSS method can be perceived as a practical solution in data analysis applications.

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Cited By

Zhou QSun B(2024)A Gaussian–Based WGAN–GP Oversampling Approach for Solving the Class Imbalance ProblemInternational Journal of Applied Mathematics and Computer Science10.61822/amcs-2024-002134:2(291-307)Online publication date: 1-Jun-2024
https://dl.acm.org/doi/10.61822/amcs-2024-0021
Lim WYong KLau BTan C(2024)Future of generative adversarial networks (GAN) for anomaly detection in network securityComputers and Security10.1016/j.cose.2024.103733139:COnline publication date: 16-May-2024
https://dl.acm.org/doi/10.1016/j.cose.2024.103733
Firdausanti NMendonça IAritsugi M(2024)Noise-free sampling with majority framework for an imbalanced classification problemKnowledge and Information Systems10.1007/s10115-024-02079-666:7(4011-4042)Online publication date: 1-Jul-2024
https://dl.acm.org/doi/10.1007/s10115-024-02079-6
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Combined oversampling and undersampling method based on slow-start algorithm for imbalanced network traffic

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Information & Contributors

Information

Published In

cover image Computing

Computing Volume 103, Issue 3

Mar 2021

174 pages

ISSN:0010-485X

Issue’s Table of Contents

© Springer-Verlag GmbH Austria, part of Springer Nature 2020.

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Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 01 March 2021

Accepted: 12 October 2020

Received: 13 September 2020

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Cited By

Zhou QSun B(2024)A Gaussian–Based WGAN–GP Oversampling Approach for Solving the Class Imbalance ProblemInternational Journal of Applied Mathematics and Computer Science10.61822/amcs-2024-002134:2(291-307)Online publication date: 1-Jun-2024
https://dl.acm.org/doi/10.61822/amcs-2024-0021
Lim WYong KLau BTan C(2024)Future of generative adversarial networks (GAN) for anomaly detection in network securityComputers and Security10.1016/j.cose.2024.103733139:COnline publication date: 16-May-2024
https://dl.acm.org/doi/10.1016/j.cose.2024.103733
Firdausanti NMendonça IAritsugi M(2024)Noise-free sampling with majority framework for an imbalanced classification problemKnowledge and Information Systems10.1007/s10115-024-02079-666:7(4011-4042)Online publication date: 1-Jul-2024
https://dl.acm.org/doi/10.1007/s10115-024-02079-6
Qin JHan XWang CHu QJiang BZhang CLu Z(2023)Network Traffic Classification Based on SD Sampling and Hierarchical Ensemble LearningSecurity and Communication Networks10.1155/2023/43743852023Online publication date: 1-Jan-2023
https://dl.acm.org/doi/10.1155/2023/4374385

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