A modified lightweight quantum convolutional neural network for malicious code detection

• Published in: Quantum science and technology Vol. 10; no. 1; pp. 15007 - 15019
• Main Authors: Xiong, Qibing, Fei, Yangyang, Du, Qiming, Zhao, Bo, Di, Shiqin, Shan, Zheng
• Format: Journal Article
• Language: English
• Published: IOP Publishing 01.01.2025
• Subjects: malicious code detection; quantum computing; quantum convolutional neural network; quantum machine learning
• ISSN: 2058-9565; 2058-9565
• DOI: 10.1088/2058-9565/ad80bd

Quantum neural network fully utilize the respective advantages of quantum computing and classical neural network, providing a new path for the development of artificial intelligence. In this paper, we propose a modified lightweight quantum convolutional neural network (QCNN), which contains a high-scalability and parameterized quantum convolutional layer and a quantum pooling circuit with quantum bit multiplexing, effectively utilizing the computational advantages of quantum systems to accelerate classical machine learning tasks. The experimental results show that the classification accuracy (precision, F1-score) of this QCNN on DataCon2020, Ember and BODMAS have been improved to 96.65% (94.3%, 96.74%), 92.4% (91.01%, 92.53%) and 95.6% (91.99%, 95.78%), indicating that this QCNN has strong robustness as well as good generalization performance for malicious code detection, which is of great significance to cyberspace security.