{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,31]],"date-time":"2026-03-31T16:44:17Z","timestamp":1774975457956,"version":"3.50.1"},"reference-count":55,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/legalcode"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Social Science Foundation of China","doi-asserted-by":"publisher","award":["20BTJ046"],"award-info":[{"award-number":["20BTJ046"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Access"],"published-print":{"date-parts":[[2020]]},"DOI":"10.1109\/access.2020.3037322","type":"journal-article","created":{"date-parts":[[2020,11,11]],"date-time":"2020-11-11T21:14:09Z","timestamp":1605129249000},"page":"204471-204482","source":"Crossref","is-referenced-by-count":5,"title":["An Integrated Model Based on O-GAN and Density Estimation for Anomaly Detection"],"prefix":"10.1109","volume":"8","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1183-2238","authenticated-orcid":false,"given":"Shuo","family":"Liu","sequence":"first","affiliation":[]},{"given":"Liwen","family":"Xu","sequence":"additional","affiliation":[]}],"member":"263","reference":[{"key":"ref39","article-title":"Anomaly detection with generative adversarial networks for multivariate time series","author":"li","year":"2018","journal-title":"arXiv 1809 04758"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-30490-4_56"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1145\/1401890.1401946"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2008.17"},{"key":"ref31","first-page":"503","article-title":"RDF: A density-based outlier detection method using vertical data representation","author":"ren","year":"2004","journal-title":"Proc of IEEE Intl Conf on Data Mining (ICDM"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1145\/342009.335388"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2006.100"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1016\/S0167-8655(99)00087-2"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1109\/TSMCC.2002.807277"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE.2003.1260802"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/NNSP.2003.1318056"},{"key":"ref27","first-page":"582","article-title":"Support vector method for novelty detection","author":"sch\u00f6lkopf","year":"1999","journal-title":"Proc Int Conf Neural Inf Process"},{"key":"ref29","author":"tarassenko","year":"2009","journal-title":"Novelty Detection"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2013.184"},{"key":"ref1","article-title":"Deep learning for anomaly detection: A survey","author":"chalapathy","year":"2019","journal-title":"arXiv 1901 03407"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00301"},{"key":"ref22","first-page":"214","article-title":"Wasserstein GAN","volume":"70","author":"arjovsky","year":"2017","journal-title":"Proc 34th Int Conf Mach Learn"},{"key":"ref21","article-title":"Unsupervised representation learning with deep convolutional generative adversarial networks","author":"radford","year":"2015","journal-title":"arXiv 1511 06434"},{"key":"ref24","article-title":"Adversarial feature learning","author":"donahue","year":"2016","journal-title":"arXiv 1605 09782"},{"key":"ref23","article-title":"Least squares generative adversarial networks","author":"mao","year":"2016","journal-title":"arXiv 1611 04076"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1145\/335191.335437"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1007\/978-0-387-73003-5_196"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-45823-6_67"},{"key":"ref51","article-title":"Kernel smoothing","volume":"54","author":"wand","year":"1994","journal-title":"Biometrics"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D18-1428"},{"key":"ref54","first-page":"8778","article-title":"Generalized cross entropy loss for training deep neural networks with noisy labels","author":"zhang","year":"2018","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1186\/1471-2105-15-S6-S2"},{"key":"ref52","first-page":"1","article-title":"Variational autoencoder based anomaly detection using reconstruction probability","author":"an","year":"2015","journal-title":"Proc Special Lect IE"},{"key":"ref10","article-title":"O-GAN: Extremely concise approach for auto-encoding generative adversarial networks","author":"su","year":"2019","journal-title":"arXiv 1903 01931"},{"key":"ref11","article-title":"GANomaly: Semi-supervised anomaly detection via adversarial training","author":"akcay","year":"2018","journal-title":"arXiv 1805 06725"},{"key":"ref40","article-title":"GAN-QP: A novel GAN framework without gradient vanishing and lipschitz constraint","author":"su","year":"2018","journal-title":"arXiv 1811 07296"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2933602"},{"key":"ref13","article-title":"Efficient GAN-based anomaly detection","author":"zenati","year":"2018","journal-title":"arXiv 1802 06222"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.19"},{"key":"ref15","author":"bishop","year":"2006","journal-title":"Pattern Recognition and Machine Learning"},{"key":"ref16","article-title":"Unsupervised Image-to-Image translation with generative adversarial networks","author":"dong","year":"2017","journal-title":"arXiv 1701 02676"},{"key":"ref17","article-title":"Learning to discover cross-domain relations with generative adversarial networks","author":"kim","year":"2017","journal-title":"arXiv 1703 05192"},{"key":"ref18","article-title":"Generative semantic manipulation with contrasting GAN","author":"liang","year":"2017","journal-title":"arXiv 1708 00315"},{"key":"ref19","first-page":"1","article-title":"SeqGAN: Sequence generative adversarial nets with policy gradient","author":"yu","year":"2017","journal-title":"Proc 31st AAAI Conf Artif Intell"},{"key":"ref4","first-page":"3002","article-title":"A state of the art survey of data mining-based fraud detection and credit scoring","author":"xun","year":"2018","journal-title":"Proc MATEC Web Conf EDP Sci"},{"key":"ref3","first-page":"593","article-title":"Review of novelty detection methods","author":"miljkovic","year":"2010","journal-title":"Proc of 33rd Intl Conv"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1155\/2016\/5642856"},{"key":"ref5","doi-asserted-by":"crossref","first-page":"677","DOI":"10.15837\/ijccc.2017.5.2972","article-title":"A new deep learning approach for anomaly base IDS using memetic classifier","volume":"12","author":"shahriar","year":"2017","journal-title":"Int J Comput Commun Control"},{"key":"ref8","first-page":"2672","article-title":"Generative adversarial nets","author":"goodfellow","year":"2014","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1016\/j.sigpro.2013.12.026"},{"key":"ref49","article-title":"MDGAN: Boosting anomaly detection using Multi-discriminator generative adversarial networks","author":"intrator","year":"2018","journal-title":"arXiv 1810 05221"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-59050-9_12"},{"key":"ref46","author":"chollet","year":"2015","journal-title":"Keras"},{"key":"ref45","first-page":"2825","article-title":"Scikit-learn: Machine learning in Python","volume":"12","author":"pedregosa","year":"2011","journal-title":"J Mach Learn Res"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2019.01.010"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2018.00088"},{"key":"ref42","author":"lecun","year":"2010","journal-title":"MNIST Handwritten Digit Database"},{"key":"ref41","author":"lichman","year":"1999","journal-title":"KDD Cup 1999 Data (University of California Irvine)"},{"key":"ref44","author":"krizhevsky","year":"2007","journal-title":"Cifar-10 (canadian institute for advanced research)"},{"key":"ref43","author":"xiao","year":"2017","journal-title":"Fashion-mnist a novel image dataset for benchmarking machine learning algorithms"}],"container-title":["IEEE Access"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/6287639\/8948470\/09256316.pdf?arnumber=9256316","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,12,17]],"date-time":"2021-12-17T19:54:55Z","timestamp":1639770895000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9256316\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"references-count":55,"URL":"https:\/\/doi.org\/10.1109\/access.2020.3037322","relation":{},"ISSN":["2169-3536"],"issn-type":[{"value":"2169-3536","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020]]}}}