{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,3]],"date-time":"2026-07-03T11:52:45Z","timestamp":1783079565657,"version":"3.54.6"},"reference-count":40,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/legalcode"}],"funder":[{"name":"science and technology program \u201cResearch on business application transformation to the cloud and full-link operation analysis technology\u201d of State Grid Corporation of China","award":["5700-202152169A-0-0-00"],"award-info":[{"award-number":["5700-202152169A-0-0-00"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Access"],"published-print":{"date-parts":[[2021]]},"DOI":"10.1109\/access.2021.3077014","type":"journal-article","created":{"date-parts":[[2021,5,3]],"date-time":"2021-05-03T20:34:26Z","timestamp":1620074066000},"page":"67249-67258","source":"Crossref","is-referenced-by-count":25,"title":["ConNet: Deep Semi-Supervised Anomaly Detection Based on Sparse Positive Samples"],"prefix":"10.1109","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2533-9380","authenticated-orcid":false,"given":"Feng","family":"Gao","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jing","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ruiying","family":"Cheng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yi","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ying","family":"Ye","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1145\/1143844.1143874"},{"key":"ref38","author":"rayana","year":"2020","journal-title":"ODDS Library"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2003.1250918"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1007\/s10994-020-05877-5"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00356"},{"key":"ref30","first-page":"21","article-title":"Detecting anomalous data using auto-encoders","volume":"6","author":"andrews","year":"2016","journal-title":"Int J Mach Learn Comput"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.324"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1145\/3184558.3186580"},{"key":"ref35","article-title":"Deep semi-supervised anomaly detection","author":"ruff","year":"2019","journal-title":"arXiv 1906 02694"},{"key":"ref34","first-page":"387","article-title":"Partially supervised classification of text documents","volume":"2","author":"liu","year":"2002","journal-title":"Proc Int Conf Mach Learn (ICML)"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1137\/1.9781611974973.11"},{"key":"ref40","first-page":"226","article-title":"A density-based algorithm for discovering clusters in large spatial databases with noise","volume":"96","author":"ester","year":"1996","journal-title":"Proc KDD"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1007\/3-540-46145-0_17"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1145\/3097983.3098052"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2018.00088"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1145\/3219819.3220042"},{"key":"ref15","first-page":"4393","article-title":"Deep one-class classification","author":"ruff","year":"2018","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref16","first-page":"622","article-title":"Ganomaly: Semi-supervised anomaly detection via adversarial training","author":"akcay","year":"2018","journal-title":"Proc Asian Conf Comput Vis"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.3390\/jimaging4020036"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1007\/s10618-011-0234-x"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1155\/2017\/8501683"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-01307-2_84"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-59050-9_12"},{"key":"ref27","first-page":"467","article-title":"Rapid distance-based outlier detection via sampling","volume":"26","author":"sugiyama","year":"2013","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/ICC.2013.6655254"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1145\/2133360.2133363"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.5244\/C.29.8"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1145\/342009.335388"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1023\/B:MACH.0000008084.60811.49"},{"key":"ref7","first-page":"582","article-title":"Support vector method for novelty detection","volume":"12","author":"sch\u00f6lkopf","year":"1999","journal-title":"Proc NIPS"},{"key":"ref2","article-title":"A comprehensive survey of data mining-based fraud detection research","author":"phua","year":"2010","journal-title":"arXiv 1009 6119"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1038\/nature14539"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1016\/j.cose.2008.08.003"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1613\/jair.3623"},{"key":"ref22","article-title":"Deep weakly-supervised anomaly detection","author":"pang","year":"2019","journal-title":"arXiv 1910 13601"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2016.2526063"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1016\/S0167-8655(03)00003-5"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1145\/3292500.3330871"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/ETTandGRS.2008.306"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1145\/276304.276314"}],"container-title":["IEEE Access"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/6287639\/9312710\/09420659.pdf?arnumber=9420659","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,12,17]],"date-time":"2021-12-17T19:55:55Z","timestamp":1639770955000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9420659\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"references-count":40,"URL":"https:\/\/doi.org\/10.1109\/access.2021.3077014","relation":{},"ISSN":["2169-3536"],"issn-type":[{"value":"2169-3536","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021]]}}}