{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,13]],"date-time":"2026-08-13T21:53:15Z","timestamp":1786657995488,"version":"3.56.0"},"reference-count":29,"publisher":"Oxford University Press (OUP)","issue":"10","license":[{"start":{"date-parts":[[2024,7,27]],"date-time":"2024-07-27T00:00:00Z","timestamp":1722038400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/pages\/standard-publication-reuse-rights"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,10,12]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Classical machine learning is more susceptible to adversarial examples due to its linear and non-robust nature, which results in a severe degradation of the recognition accuracy of classical machine learning models. Quantum techniques are shown to have a higher robustness advantage and are more resistant to attacks from adversarial examples than classical machine learning. Inspired by the robustness advantage of quantum computing and the feature extraction advantage of convolutional neural networks, this paper proposes a novel variational quantum convolutional neural network model (VQCNN), whose quantum fully connected layer consists of a combination of a quantum filter and a variational quantum neural network to increase the model\u2019s adversarial robustness. The network intrusion detection model based on VQCNN is verified on KDD CUP99 and UNSW-NB datasets. The results show that under the attack of Fast Gradient Sign Method, the decline values of accuracy, precision, and recall rate of the intrusion detection model based on VQCNN are less than those of the other four models, and it has higher adversarial robustness.<\/jats:p>","DOI":"10.1093\/comjnl\/bxae062","type":"journal-article","created":{"date-parts":[[2024,7,28]],"date-time":"2024-07-28T05:08:01Z","timestamp":1722143281000},"page":"2970-2983","source":"Crossref","is-referenced-by-count":5,"title":["VQCNN: variational quantum convolutional neural networks based on quantum filters and fully connected layers"],"prefix":"10.1093","volume":"67","author":[{"given":"Han","family":"Qi","sequence":"first","affiliation":[{"name":"Shenyang Aerospace University School of Computer Science and Technology, , No. 37 Daoyi South Avenue, Shenbei New Area, Shenyang, 110136,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jingtong","family":"Wang","sequence":"additional","affiliation":[{"name":"Shenyang Aerospace University School of Computer Science and Technology, , No. 37 Daoyi South Avenue, Shenbei New Area, Shenyang, 110136,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yufan","family":"Cui","sequence":"additional","affiliation":[{"name":"Shenyang Aerospace University School of Computer Science and Technology, , No. 37 Daoyi South Avenue, Shenbei New Area, Shenyang, 110136,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2024,7,27]]},"reference":[{"key":"2024101809313833200_ref1","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1109\/MALWARE.2015.7413680","article-title":"Deep neural network based malware detection using two-dimensional binary program features","volume-title":"2015 10th International Conference on Malicious and Unwanted Software","author":"Saxe","year":"2015"},{"key":"2024101809313833200_ref2","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1145\/3065386","article-title":"Imagenet classification with deep convolutional neural networks","volume":"60","author":"Krizhevsky","year":"2017","journal-title":"Commun ACM"},{"key":"2024101809313833200_ref3","first-page":"445","article-title":"The influence of the amount of parameters in different layers on the performance of deep learning models","volume":"5","author":"Yue","year":"2015","journal-title":"Comput Sci Appl"},{"key":"2024101809313833200_ref4","doi-asserted-by":"crossref","first-page":"1440","DOI":"10.1109\/ICCV.2015.169","article-title":"fast r-cnn","volume-title":"2015 IEEE International Conference on Computer Vision (ICCV)","author":"Girshick","year":"2015"},{"key":"2024101809313833200_ref5","first-page":"December","article-title":"Intriguing properties of neural networks","volume-title":"2nd International Conference on Learning Representations","author":"Szegedy","year":"2014"},{"key":"2024101809313833200_ref6","article-title":"Explaining and harnessing adversarial examples","volume-title":"3rd International Conference on Learning Representations","author":"Goodfellow","year":"2015"},{"key":"2024101809313833200_ref7","doi-asserted-by":"crossref","first-page":"79","DOI":"10.22331\/q-2018-08-06-79","article-title":"Quantum computing in the nisq era and beyond","volume":"2","author":"Preskill","year":"2018","journal-title":"Quantum"},{"key":"2024101809313833200_ref8","doi-asserted-by":"crossref","first-page":"828","DOI":"10.3390\/e22080828","article-title":"Hybrid quantum-classical neural network for calculating ground state energies of molecules","volume":"22","author":"Xia","year":"2020","journal-title":"Entropy"},{"key":"2024101809313833200_ref9","first-page":"060501","article-title":"Experimental implementation of a quantum autoencoder via quantum adders","volume":"2","author":"Ding","year":"2019","journal-title":"Wiley"},{"key":"2024101809313833200_ref10","article-title":"Quantum approximate optimization algorithm: performance, mechanism, and implementation on near-term devices","volume":"10","author":"Zhou","year":"2020","journal-title":"Phys Rev X"},{"key":"2024101809313833200_ref11","doi-asserted-by":"crossref","first-page":"1273","DOI":"10.1038\/s41567-019-0648-8","article-title":"Quantum convolutional neural networks","volume":"15","author":"Cong","year":"2019","journal-title":"Nat. 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