{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,17]],"date-time":"2025-09-17T14:49:58Z","timestamp":1758120598250,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":21,"publisher":"Springer Nature Singapore","isbn-type":[{"type":"print","value":"9789819980727"},{"type":"electronic","value":"9789819980734"}],"license":[{"start":{"date-parts":[[2023,11,15]],"date-time":"2023-11-15T00:00:00Z","timestamp":1700006400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,11,15]],"date-time":"2023-11-15T00:00:00Z","timestamp":1700006400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024]]},"DOI":"10.1007\/978-981-99-8073-4_6","type":"book-chapter","created":{"date-parts":[[2023,11,14]],"date-time":"2023-11-14T08:02:54Z","timestamp":1699948974000},"page":"69-82","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Quantum Autoencoder Frameworks for\u00a0Network Anomaly Detection"],"prefix":"10.1007","author":[{"given":"Moe","family":"Hdaib","sequence":"first","affiliation":[]},{"given":"Sutharshan","family":"Rajasegarar","sequence":"additional","affiliation":[]},{"given":"Lei","family":"Pan","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,11,15]]},"reference":[{"issue":"1","key":"6_CR1","first-page":"1","volume":"2","author":"J An","year":"2015","unstructured":"An, J., Cho, S.: Variational autoencoder based anomaly detection using reconstruction probability. Spec. Lect. IE 2(1), 1\u201318 (2015)","journal-title":"Spec. Lect. IE"},{"issue":"1","key":"6_CR2","doi-asserted-by":"publisher","first-page":"53","DOI":"10.1016\/0893-6080(89)90014-2","volume":"2","author":"P Baldi","year":"1989","unstructured":"Baldi, P., Hornik, K.: Neural networks and principal component analysis: learning from examples without local minima. Neural Netw. 2(1), 53\u201358 (1989)","journal-title":"Neural Netw."},{"key":"6_CR3","volume-title":"Pattern Recognition and Machine Learning","author":"CM Bishop","year":"2006","unstructured":"Bishop, C.M., Nasrabadi, N.M.: Pattern Recognition and Machine Learning. Springer, New York (2006)"},{"key":"6_CR4","doi-asserted-by":"crossref","unstructured":"De Oliveira, N.M., Lucas, P., De Oliveira, W.R., Ludermir, T.B., Da Silva, A.J.: Quantum one-class classification with a distance-based classifier. In: Proceedings of the International Joint Conference on Neural Networks (IJCNN), pp. 1\u20137. IEEE (2021)","DOI":"10.1109\/IJCNN52387.2021.9533441"},{"key":"6_CR5","unstructured":"Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013)"},{"issue":"4","key":"6_CR6","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevResearch.3.043184","volume":"3","author":"K Kottmann","year":"2021","unstructured":"Kottmann, K., Metz, F., Fraxanet, J., Baldelli, N.: Variational quantum anomaly detection: unsupervised mapping of phase diagrams on a physical quantum computer. Phys. Rev. Res. 3(4), 043184 (2021)","journal-title":"Phys. Rev. Res."},{"key":"6_CR7","unstructured":"Kyriienko, O., Magnusson, E.B.: Unsupervised quantum machine learning for fraud detection (2022)"},{"issue":"5","key":"6_CR8","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevA.99.052310","volume":"99","author":"JM Liang","year":"2019","unstructured":"Liang, J.M., Shen, S.Q., Li, M., Li, L.: Quantum anomaly detection with density estimation and multivariate Gaussian distribution. Phys. Rev. A 99(5), 052310 (2019)","journal-title":"Phys. Rev. A"},{"key":"6_CR9","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2020.105753","volume":"196","author":"P Lv","year":"2020","unstructured":"Lv, P., Yu, Y., Fan, Y., Tang, X., Tong, X.: Layer-constrained variational autoencoding kernel density estimation model for anomaly detection. Knowl.-Based Syst. 196, 105753 (2020)","journal-title":"Knowl.-Based Syst."},{"issue":"2","key":"6_CR10","doi-asserted-by":"publisher","first-page":"13","DOI":"10.1007\/s42484-022-00070-4","volume":"4","author":"S Mangini","year":"2022","unstructured":"Mangini, S., Marruzzo, A., Piantanida, M., Gerace, D., Bajoni, D., Macchiavello, C.: Quantum neural network autoencoder and classifier applied to an industrial case study. Quantum Mach. Intell. 4(2), 13 (2022). https:\/\/doi.org\/10.1007\/s42484-022-00070-4","journal-title":"Quantum Mach. Intell."},{"key":"6_CR11","doi-asserted-by":"crossref","unstructured":"McClelland, J.L., Rumelhart, D.E., Group, P.R., et al.: Parallel Distributed Processing, Volume 2: Explorations in the Microstructure of Cognition: Psychological and Biological Models, vol. 2. MIT Press, Cambridge (1987)","DOI":"10.7551\/mitpress\/5237.001.0001"},{"key":"6_CR12","unstructured":"Mete, B., Gutierrez, I.L., Mendl, C.: Hamiltonian simulation using quantum autoencoders (2021)"},{"key":"6_CR13","unstructured":"Plaut, E.: From principal subspaces to principal components with linear autoencoders. arXiv preprint arXiv:1804.10253 (2018)"},{"key":"6_CR14","doi-asserted-by":"crossref","unstructured":"Pol, A.A., Berger, V., Germain, C., Cerminara, G., Pierini, M.: Anomaly detection with conditional variational autoencoders. In: Proceedings of the 2019 18th IEEE International Conference on Machine Learning and Applications (ICMLA), pp. 1651\u20131657. IEEE (2019)","DOI":"10.1109\/ICMLA.2019.00270"},{"key":"6_CR15","doi-asserted-by":"crossref","unstructured":"Ranzato, M., Huang, F.J., Boureau, Y.L., LeCun, Y.: Unsupervised learning of invariant feature hierarchies with applications to object recognition. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 1\u20138. IEEE (2007)","DOI":"10.1109\/CVPR.2007.383157"},{"issue":"4","key":"6_CR16","doi-asserted-by":"publisher","DOI":"10.1088\/2058-9565\/aa8072","volume":"2","author":"J Romero","year":"2017","unstructured":"Romero, J., Olson, J.P., Aspuru-Guzik, A.: Quantum autoencoders for efficient compression of quantum data. Quantum Sci. Technol. 2(4), 045001 (2017)","journal-title":"Quantum Sci. Technol."},{"key":"6_CR17","unstructured":"Ruff, L., et al.: Deep one-class classification. In: Proceedings of the 2018 International Conference on Machine Learning, pp. 4393\u20134402. PMLR (2018)"},{"issue":"7","key":"6_CR18","doi-asserted-by":"publisher","first-page":"1443","DOI":"10.1162\/089976601750264965","volume":"13","author":"B Sch\u00f6lkopf","year":"2001","unstructured":"Sch\u00f6lkopf, B., Platt, J.C., Shawe-Taylor, J., Smola, A.J., Williamson, R.C.: Estimating the support of a high-dimensional distribution. Neural Comput. 13(7), 1443\u20131471 (2001)","journal-title":"Neural Comput."},{"key":"6_CR19","doi-asserted-by":"publisher","first-page":"471","DOI":"10.1023\/A:1024022632303","volume":"1","author":"CA Trugenberger","year":"2002","unstructured":"Trugenberger, C.A.: Quantum pattern recognition. Quantum Inf. Process. 1, 471\u2013493 (2002). https:\/\/doi.org\/10.1023\/A:1024022632303","journal-title":"Quantum Inf. Process."},{"key":"6_CR20","unstructured":"Useche, D.H., Bustos-Brinez, O.A., Gallego, J.A., Gonz\u00e1lez, F.A.: Computing expectation values of adaptive Fourier density matrices for quantum anomaly detection in NISQ devices. arXiv: 2201.10006 (2022)"},{"key":"6_CR21","unstructured":"Wang, M.M., Jiang, Y.D.: Data reconstruction based on quantum neural networks. arXiv preprint arXiv:2209.05711 (2022)"}],"container-title":["Lecture Notes in Computer Science","Neural Information Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-99-8073-4_6","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,13]],"date-time":"2024-03-13T18:36:05Z","timestamp":1710354965000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-99-8073-4_6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,11,15]]},"ISBN":["9789819980727","9789819980734"],"references-count":21,"URL":"https:\/\/doi.org\/10.1007\/978-981-99-8073-4_6","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2023,11,15]]},"assertion":[{"value":"15 November 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICONIP","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Neural Information Processing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Changsha","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"20 November 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 November 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"30","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iconip2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/iconip2023.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"1274","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"650","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"51% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"4.14","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"2.46","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}