Malicious Code Detection Method Based on Hybrid Quantum Convolutional Neural Network

Quantum computing is a new computing model based on quantum mechanics,with powerful parallel computing capabi-lity far beyond classical computing.Hybrid quantum convolutional neural network combines the dual advantages of quantum computing and classical convolutional neural network,and gradually becomes one of the research hotspots in the field of quantum machine learning.Currently,the scale of malicious code is still growing at a high speed,its detection model is getting more and more complex,the number of parameters is getting bigger and bigger,and there is an urgent need for an efficient and lightweight detection model.For this,this paper designs a hybrid quantum convolutional neural network model,which integrates quantum computing into classical convolutional neural network to improve the computational efficiency of the model.The model contains a quantum convolutional layer,a pooling layer,and a classical fully connected layer.The quantum convolutional layer is implemented using a low-depth,strong entangled and lightweight parameterized quantum circuit,using only two types of quantum gates:quantum rotation gate Ry and CNOT(controlled-NOT),and using only two qubits to implement the convolutional computation.The pooling layer implements three pooling methods based on classical and quantum computation.The simulation experiments in this paper are conducted on Google TensorFlow Quantum.Experimental results show that the classification performance(accuracy,F1-score) of the model in this paper on the open-source malicious code datasets DataCon2020 and Ember,reaches(97.75%,97.71%) and(94.65%,94.78%),which are both significantly improved.