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Network intrusion detection based on variational quantum convolution neural network

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Abstract

With the rapid development of quantum machine learning (QML), quantum convolutional neural networks (QCNN) have been proposed and shown advantages in classification problems. An intrusion detection system (IDS) based on the QML method is proven to have higher accuracy than IDS based on the traditional machine learning (ML) method. However, the multiple convolution pooling operations of QCNN will cause the loss of valuable data features, resulting in a large error in the final measurement results. In this paper, we design an IDS model of QCNN based on a variational quantum neural network (VQNN), which can effectively reduce data feature loss and improve detection accuracy. We compare this model with traditional ML models such as artificial neural network (ANN), logistic regression (LR), K-nearest neighbor (KNN) algorithm, support vector machine (SVM), and decision tree (DT). Experiment results show that the accuracy of our proposed model is 94.51%, which is higher than other classical IDS models.

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https://research.unsw.edu.au/projects/unsw-nb15-dataset

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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.

Funding

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, Hongsheng Zhu & Han Qi

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

    Abdullah Gani

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Appendices

Appendix A: QCNN

The circuit structure of QCNN model applied in this paper is shown in Fig. 13. Figure 13 is a 10-qubit QCNN model with multiple convolution filters and pooling operations in each layer. Among them, the convolution filter we selected is circuit 10 in Fig. 6, and the pooling circuit we selected is (b) in Fig. 7. Given a quantum state \(\rho _\textrm{in} \), after four layers of convolution pooling operation, the user-defined cost function is calculated using the measurement results of the quantum circuit. The classical computer is used to calculate a new set of parameters based on gradient, and the parameters of the subsequent round of quantum circuit are updated accordingly.

Fig. 13
figure 13

The circuit structure of QCNN model

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Appendix B: The experimental results of 10 convolution circuits

In this part, we give the experimental results of these 10 convolution circuits. For each combination of convolution and pooling operations, five groups of data are obtained from the random initialization of trainable parameters, and Tables 8, 9, 10, and 11 are obtained. They are divided into two groups. Tables 8 and 9 show the accuracy and FAR obtained by applying the first pooling operation to the VQNN–QCNN model. Tables 10 and 11 show the accuracy and FAR obtained by applying the second pooling operation to the VQNN–QCNN model. And we give the time for each combination to iterate over one time dataset.

From Tables 8 and 10, it can be seen that the experimental effect is improved with the increase in parameters. The best experimental effect is circuit 9, and the worst experimental effects are circuit 1 and circuit 2. However, the iteration time of circuit 7, circuit 8, and circuit 9 is too long. So we finally choose convolution circuit 10 as the convolution filter of the VQNN–QCNN model.

Table 8 Accuracy obtained by applying the first pooling operation
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Table 9 FAR obtained by applying the first pooling operation
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Table 10 Accuracy obtained by applying the second pooling operation
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Table 11 FAR obtained by applying the second pooling operation
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Gong, C., Guan, W., Zhu, H. et al. Network intrusion detection based on variational quantum convolution neural network. J Supercomput 80, 12743–12770 (2024). https://doi.org/10.1007/s11227-024-05919-y

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