Hybrid Continuous Variational Quantum Neural Networks for Network Intrusion Detection

Abstract

In order to solve the problem of excessive parameters and slow computing speed of classical neural networks, this paper uses the powerful parallel computing power of quantum computing to improve the computing speed of classical neural network models, and proposes a hybrid continuous variational quantum neural network (HCVQNN) model that can be used for network intrusion detection. The continuous variable layer of the model is realized through Gaussian gate and non-Gaussian gate, in which Gaussian gate performs operations such as quantum state weight addition and offset term setting, and non-Gaussian gate performs nonlinear operations, thereby improving the overall expression ability of the network model. In addition, aiming at the unbalanced problem of UNSW-NB15 in the network intrusion dataset, this paper proposes to design the algorithm from the feature level and the algorithm level, using the ReliefF algorithm for feature selection at the feature level, and oversampling and undersampling processing at the algorithm level by combining the Borderline-SMOTE algorithm and GMM algorithm. The classification experimental results on the UNSW-NB15 network intrusion dataset show that compared with the other two network models, the HCVQNN network model obtains higher classification accuracy and lower loss function value in both binary classification and multi-classification tasks, and the classification accuracy for minority categories is also improved.