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A few-shot learning-based Siamese capsule network for intrusion detection with imbalanced training data

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journal contribution
posted on 14.09.2021, 15:11 by Zu-Min Wang, Ji-Yu Tian, Jing Qin, Hui FangHui Fang, Li-Ming Chen
Network intrusion detection remains one of the major challenges in cybersecurity. In recent years, many machine-learning-based methods have been designed to capture the dynamic and complex intrusion patterns to improve the performance of intrusion detection systems. However, two issues, including imbalanced training data and new unknown attacks, still hinder the development of a reliable network intrusion detection system. In this paper, we propose a novel few-shot learning-based Siamese capsule network to tackle the scarcity of abnormal network traffic training data and enhance the detection of unknown attacks. In specific, the well-designed deep learning network excels at capturing dynamic relationships across traffic features. In addition, an unsupervised subtype sampling scheme is seamlessly integrated with the Siamese network to improve the detection of network intrusion attacks under the circumstance of imbalanced training data. Experimental results have demonstrated that the metric learning framework is more suitable to extract subtle and distinctive features to identify both known and unknown attacks after the sampling scheme compared to other supervised learning methods. Compared to the state-of-the-art methods, our proposed method achieves superior performance to effectively detect both types of attacks.

Funding

Youth Fund Project of the National Nature Fund of China under grant 62002038

History

School

  • Science

Department

  • Computer Science

Published in

Computational Intelligence and Neuroscience

Volume

2021

Publisher

Hindawi Publishing Corporation

Version

VoR (Version of Record)

Rights holder

© The Authors

Publisher statement

This is an Open Access Article. It is published by Hindawi under the Creative Commons Attribution 4.0 International Licence (CC BY 4.0). Full details of this licence are available at: https://creativecommons.org/licenses/by/4.0/

Acceptance date

30/08/2021

Publication date

2021-09-14

Copyright date

2021

ISSN

1687-5265

eISSN

1687-5273

Language

en

Depositor

Dr Hui Fang. Deposit date: 31 August 2021

Article number

7126913