Network intrusion detection based on variational quantum convolution neural network

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