计算机科学

计算机科学 ›› 2025, Vol. 52 ›› Issue (3): 385-390.doi: 10.11896/jsjkx.240800006

• 信息安全 • 上一篇    下一篇

一种基于混合量子卷积神经网络的恶意代码检测方法

熊其冰1,2, 苗启广3, 杨天1, 袁本政1, 费洋扬1   

  1. 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
  • 收稿日期:2024-08-02 修回日期:2024-12-09 出版日期:2025-03-15 发布日期:2025-03-07
  • 通讯作者: 费洋扬(feiyangyang@pku.edu.cn)
  • 作者简介:(xiongqb1990@163.com)
  • 基金资助:
    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)

Malicious Code Detection Method Based on Hybrid Quantum Convolutional Neural Network

XIONG Qibing1,2, MIAO Qiguang3, YANG Tian1, YUAN Benzheng1, FEI Yangyang1   

  1. 1 School of Network and Cybersecurity,Information Engineering University,Zhengzhou 450000,China
    2 Department of Network Security,Henan Police College,Zhengzhou 450000,China
    3 School of Computer Science and Technology,Xidian University,Xi’an 710071,China
  • Received:2024-08-02 Revised:2024-12-09 Online:2025-03-15 Published:2025-03-07
  • About author:XIONG Qibing,born in 1990,Ph.D candidate,lecturer.His main research interests include quantum computing and cyberspace security.
    FEI Yangyang,born in 1990,Ph.D.His main research interests include quantum information and quantum computation.
  • Supported by:
    National Natural Science Foundation of China(61901525),Henan Police College Funding Project(HNJY-2024-QN-03),Key Technology Research and Development Program of Henan Province(232102211031) and Training Program for Young Backbone Teachers of Higher Education Institutions in Henan Province(2024GGJS147).

PDF (PC)

190

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。

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

Abstract: 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.

Key words: Quantum computing, Quantum machine learning, Hybrid quantum convolutional neural network, Malicious code detection

中图分类号: 

  • TP311
[1]HE K M,ZHANG X Y,REN S Q,et al.Deep Residual Learning for Image Recognition[C]//2016 IEEE Conference on Computer Vision and Pattern Recognition(CVPR).2016:770-778.
[2]CUI Z H,DU L,WANG P H,et al.Malicious code detection based on CNNs and multi-objective algorithm[J].Journal of Parallel and Distributed Computing,2019,129:50-58.
[3]LIU T G,MIAO Q G,XU P F,et al.Superpixel-Based Shallow Convolutional Neural Network(SSCNN) for Scanned Topographic Map Segmentation[J].Remote Sensing,2020,12(20):3421.
[4]XIONG Q B,DING X,FEI Y Y,et al.A hybrid quantum ensemble learning model for malicious code detection[J].Quantum Science and Technology,2024,9(3):035021.
[5]FENG C C,ZHAO B,ZHOU X,et al.An Enhanced Quantum K-Nearest Neighbor Classification Algorithm Based on Polar Distance[J].Entropy,2023,25(1):127.
[6]FARHI E,NEVEN H.Classification with quantum neural networks on near term processors[J].arXiv:1802.06002,2018.
[7]AV-TEST Institute.AV-Test IT Security Institute Website[OL].https://portal.avatlas.org/malware
[8]DI S Q,XU J C,SHU G Q,et al.Amplitude transformed quantum convolutional neural network[J].Applied Intelligence,2023,53(18):20863-20873.
[9]CONG I,CHOI S,LUKIN M D.Quantum Convolutional Neural Networks[J].Nature Physics,2019,15(12):1273-1278.
[10]LIU J,LIM K H,WOOD K L,et al.Hybrid Quantum-Classical Convolutional Neural Networks[J].Science China Physics,Mechanics & Astronomy,2021,64(9):290311.
[11]HUR T,KIM L,PARK D K.Quantum convolutional neuralnetwork for classical data classification[J].Quantum Machine Intelligence,2022,4(1):3.
[12]ZAMAN K,AHMED T,HANIF M A,et al.A ComparativeAnalysis of Hybrid-Quantum Classical Neural Networks[J].arXiv:2402.10540,2024.
[13]LI Y,ZHOU R G,XU R,et al.A quantum deep convolutional neural network for image recognition[J].Quantum Science and Technology,2020,5(4):044003.
[14]OH S,CHOI J,KIM J K,et al.Quantum Convolutional Neural Network for Resource-Efficient Image Classification:A Quantum Random Access Memory(QRAM) Approach[C]//2021 International Conference on Information Networking(ICOIN).Jeju Island,Korea(South):IEEE,2021:50-52.
[15]KERENIDIS I,LANDMAN J,PRAKASH A.Quantum algo-rithms for deep convolutional neural networks[J].arXiv:1911.01117,2019.
[16]GONG L H,PEI J J,ZHANG T F,et al.Quantum convolutional neural network based on variational quantum circuits[J].Optics Communications,2024,550:129993.
[17]DataCon社区.DataCon开放数据集-DataCon2020-恶意代码数据集方向开放数据集[DB/OL].(2021-11-11)[2024-02-10].https://datacon.qianxin.com/opendata/openpage?resourcesId=5.
[18]ANDERSON H S,ROTH P.Ember:an open dataset for trai-ning static pe malware machine learning models[J].arXiv:1804.04637,2018.
[19]GRANT E,BENEDETTI M,CAO S,et al.Hierarchical quan-tum classifiers[J].npj Quantum Information,2018,4(1):65.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!
渝ICP备16013121号-4
地址：重庆市渝北区洪湖西路18号    邮编：401121  
电话：023-63500828（编辑部） 023-67039612（会议/专题） 023-67039623（财务/发票）
E-mail：jsjkx12@163.com
本系统由北京玛格泰克科技发展有限公司设计开发