一种基于混合量子卷积神经网络的恶意代码检测方法  

Malicious Code Detection Method Based on Hybrid Quantum Convolutional Neural Network

作　　者：熊其冰 苗启广[3] 杨天 袁本政 费洋扬 XIONG Qibing;MIAO Qiguang;YANG Tian;YUAN Benzheng;FEI Yangyang(School of Network and Cybersecurity,Information Engineering University,Zhengzhou 450000,China;Department of Network Security,Henan Police College,Zhengzhou 450000,China;School of Computer Science and Technology,Xidian University,Xi’an 710071,China)

机构地区：[1]信息工程大学网络空间安全学院,郑州450000 [2]河南警察学院网络安全系,郑州450000 [3]西安电子科技大学计算机科学与技术学院,西安710071

出　　处：《计算机科学》2025年第3期385-390,共6页Computer Science

基　　金：国家自然科学基金(61901525);河南警察学院院级课题资助项目(HNJY-2024-QN-03);河南省科技攻关项目(232102211031);河南省高等学校青年骨干教师培养计划(2024GGJS147)。

摘　　要：量子计算是基于量子力学的全新计算模式,具有远超经典计算的强大并行计算能力。混合量子卷积神经网络结合了量子计算和经典卷积神经网络的双重优势,逐渐成为量子机器学习领域的研究热点之一。当前,恶意代码规模依然呈高速增长态势,检测模型越来越复杂,参数量越来越大,迫切需要一种高效轻量型的检测模型。为此,设计了一种混合量子卷积神经网络模型,将量子计算融入经典卷积神经网络,以提高模型的计算效率。该模型包含量子卷积层、池化层和经典全连接层。量子卷积层采用低深度强纠缠轻量型的参数化量子线路实现,仅使用两类量子门:量子旋转门Ry和受控非门CNOT(controlled-NOT),并仅使用两量子比特实现卷积计算。池化层基于经典计算和量子计算实现了3种池化方法。在Google TensorFlow Quantum上进行了模拟实验。实验结果显示,所提模型在恶意代码公开数据集DataCon2020和Ember的分类性能(accuracy,F1-score)分别达到了(97.75%,97.71%)和(94.65%,94.78%),均有明显提升。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.

关 键 词：量子计算 量子机器学习 混合量子卷积神经网络 恶意代码检测 

分 类 号：TP311[自动化与计算机技术—计算机软件与理论]