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Amplification methods to promote the attacks against machine learning-based intrusion detection systems

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Abstract

The security of machine learning attracts increasing attention in both academia and industry due to its vulnerability to adversarial examples. However, the research on adversarial examples in intrusion detection is currently in its infancy. In this paper, two novel adversarial attack amplification methods based on a unified framework are proposed to promote the attack performance of the classic white-box attack methods. The proposed methods shield the underlying implementation details of the target attack methods and can effectively boost different target attack methods through a unified interface. The proposed methods extract the original adversarial perturbations from the adversarial examples produced by the target attack methods and amplify the original adversarial perturbations to generate the amplified adversarial examples. The preliminary experimental results show that the proposed methods can effectively improve the attack performance of the classic white-box attack methods. Besides, the amplified adversarial examples crafted by the proposed methods show excellent transferability across different machine learning classifiers, which ensures that the application of the proposed methods is not limited to the white-box setting. Consequently, the proposed methods can be utilized to better assess the robustness of the machine learning-based intrusion detection systems against adversarial examples in various contexts.

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Availability of data and materials

The data used to support the findings of this study is available from the corresponding author upon request.

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The code used to support the findings of this study is available from the corresponding author upon request.

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Acknowledgements

This work was supported in part by the Central Government Guides Local Science and Technology Development Special Funds, China, under grant No.[2018]4008, in part by the Science and Technology Planned Project of Guizhou Province, China, under grant No.[2020]2Y013, in part by the Science and Technology Planned Project of Guizhou Province, China, under Grant [2023]YB449. I would like to give my most sincere thanks to Prof. Xie, Xiaoyao, Prof. Xu, Yang, and Zhang, Xinyu for their generous assistance and valuable suggestions.

Funding

This work was supported in part by the Central Government Guides Local Science and Technology Development Special Funds, China, under grant No.[2018]4008, in part by the Science and Technology Planned Project of Guizhou Province, China, under grant No.[2020]2Y013, in part by the Science and Technology Planned Project of Guizhou Province, China, under Grant [2023]YB449.

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Authors and Affiliations

  1. Key Laboratory of Information and Computing Science Guizhou Province, Guizhou Normal University, 116 Baoshan North Road, Guiyang, 550001, Guizhou, China

    Sicong Zhang, Yang Xu, Xinyu Zhang & Xiaoyao Xie

  2. Cyber Science and Technology, Guizhou Normal University, Huaxi University Town, Guian New District, Guiyang, 550025, Guizhou, China

    Sicong Zhang, Yang Xu, Xinyu Zhang & Xiaoyao Xie

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  1. Sicong Zhang
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Correspondence to Yang Xu.

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Zhang, S., Xu, Y., Zhang, X. et al. Amplification methods to promote the attacks against machine learning-based intrusion detection systems. Appl Intell 54, 2941–2961 (2024). https://doi.org/10.1007/s10489-024-05311-6

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