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Network attack detection scheme based on variational quantum neural network

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Abstract

Network attack may have a serious impact on network security. With the rapid development of quantum machine learning, variational quantum neural network (VQNN) has demonstrated quantum advantages in classification problems. The intrusion detection system (IDS) based on quantum machine learning has higher accuracy and efficiency than the IDS based on traditional machine learning. In this work, we propose a intrusion detection scheme based on VQNN, which is composed of variational quantum circuit (VQC) and classical machine learning (ML) strategy. In order to verify the effectiveness of the scheme, we used the VQNN model and some classic ML models (Such as artificial neural network, support vector machines, K-Nearest Neighbors, Naive Bayes, decision tree) to conduct comparative experiments. The results indicate that the proposed IDS model based on VQNN has a 97.21% precision, which is higher than other classic IDS models. Furthermore, our VQC can be deployed on the overwhelming majority of recent noisy intermediate-scale quantum machines (such as IBM). This research will contribute to the construction of general variational quantum framework and experimental design and highlight the potential hopes and challenges of hybrid quantum classical learning schemes.

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Acknowledgements

This work was funded in part by the Liaoning Provincial Department of Education Research under Grant LJKZ0208, in part by the Scientific Research Foundation for Advanced Talents from Shenyang Aerospace University under Grant 18YB06, and National Basic Research Program of China Under Grant JCKY2018410C004.

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Authors and Affiliations

  1. School of Computer Science, Shenyang Aerospace University, Shenyang, 110000, People’s Republic of China

    Changqing Gong, Weiqi Guan & Han Qi

  2. Faculty of Computing and Informatics, University Malaysia Sabah, Labuan International Campus, 87000, Labuan, Malaysia

    Abdullah Gani

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  1. Changqing Gong
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  2. Weiqi Guan
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  4. Han Qi
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Correspondence to Han Qi.

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Gong, C., Guan, W., Gani, A. et al. Network attack detection scheme based on variational quantum neural network. J Supercomput 78, 16876–16897 (2022). https://doi.org/10.1007/s11227-022-04542-z

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