{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,10]],"date-time":"2026-07-10T16:43:31Z","timestamp":1783701811008,"version":"3.55.0"},"reference-count":46,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62033005"],"award-info":[{"award-number":["62033005"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62273270"],"award-info":[{"award-number":["62273270"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62373328"],"award-info":[{"award-number":["62373328"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100007128","name":"Natural Science Foundation of Shaanxi Province","doi-asserted-by":"publisher","award":["2023JC-XJ-17"],"award-info":[{"award-number":["2023JC-XJ-17"]}],"id":[{"id":"10.13039\/501100007128","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans.Inform.Forensic Secur."],"published-print":{"date-parts":[[2025]]},"DOI":"10.1109\/tifs.2024.3515809","type":"journal-article","created":{"date-parts":[[2024,12,11]],"date-time":"2024-12-11T22:29:47Z","timestamp":1733956187000},"page":"709-723","source":"Crossref","is-referenced-by-count":14,"title":["Detection of False Data Injection Attacks in Smart Grids: An Optimal Transport-Based Reliable Self-Training Approach"],"prefix":"10.1109","volume":"20","author":[{"given":"Kaiyao","family":"Miao","sequence":"first","affiliation":[{"name":"School of Cyber Science and Engineering, Xi’an Jiaotong University, Xi’an, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6498-6951","authenticated-orcid":false,"given":"Meng","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Cyber Science and Engineering, Xi’an Jiaotong University, Xi’an, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1721-266X","authenticated-orcid":false,"given":"Fanghong","family":"Guo","sequence":"additional","affiliation":[{"name":"Department of Automation, Zhejiang University of Technology, Hangzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5720-0941","authenticated-orcid":false,"given":"Rongxing","family":"Lu","sequence":"additional","affiliation":[{"name":"Faculty of Computer Science, University of New Brunswick, Fredericton, NB, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8826-0362","authenticated-orcid":false,"given":"Xiaohong","family":"Guan","sequence":"additional","affiliation":[{"name":"School of Cyber Science and Engineering, Xi’an Jiaotong University, Xi’an, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/TIFS.2022.3226868"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/TSG.2022.3141803"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/TSG.2022.3215579"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/TIFS.2022.3216948"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/TIFS.2022.3173728"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/TIFS.2018.2854745"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/TSG.2017.2675960"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2019.2916670"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2020.3005926"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/TSG.2023.3261970"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2018.2825243"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2020.2991693"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/SmartGridComm52983.2022.9961017"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/TSG.2023.3286697"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/TSG.2020.3010510"},{"key":"ref16","first-page":"1","article-title":"DivideMix: Learning with noisy labels as semi-supervised learning","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Li"},{"key":"ref17","first-page":"1","article-title":"Freematch: Self-adaptive thresholding for semi-supervised learning","volume-title":"Proc. 11th Int. Conf. Learn. Represent.","author":"Wang"},{"key":"ref18","first-page":"10065","article-title":"Sinkhorn label allocation: Semi-supervised classification via annealed self-training","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Tai"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/TDSC.2021.3118636"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2019.10.014"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/TSG.2021.3061395"},{"key":"ref22","first-page":"32424","article-title":"Debiased self-training for semi-supervised learning","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"35","author":"Chen"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/TCNS.2014.2357531"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/TSG.2021.3117977"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/TSG.2019.2892423"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2024.3461718"},{"issue":"2","key":"ref27","first-page":"896","article-title":"Pseudo-label: The simple and efficient semi-supervised learning method for deep neural networks","volume-title":"Proc. Int. Conf. Mach. Learn. (ICML)","volume":"3","author":"Lee"},{"key":"ref28","first-page":"11525","article-title":"Dash: Semi-supervised learning with dynamic thresholding","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Xu"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00956"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/TSG.2015.2388545"},{"key":"ref31","article-title":"Matpower user\u2019s manual version 7.0 B1","author":"Zimmerman","year":"2018"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1109\/TSG.2022.3231728"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1201\/9780203913673"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1109\/TSG.2022.3204796"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1109\/TSG.2015.2495133"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1145\/1952982.1952995"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1109\/TPWRS.2022.3152801"},{"key":"ref38","first-page":"7673","article-title":"On unbalanced optimal transport: An analysis of sinkhorn algorithm","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Pham"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52733.2024.02519"},{"key":"ref40","first-page":"1","article-title":"Sinkhorn distances: Lightspeed computation of optimal transport","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"26","author":"Cuturi"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2009.5459199"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1137\/060659624"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1109\/ISRCS.2014.6900095"},{"key":"ref44","first-page":"3271","article-title":"Multi-agent reinforcement learning for active voltage control on power distribution networks","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"34","author":"Wang"},{"issue":"11","key":"ref45","first-page":"2579","article-title":"Visualizing data using t-SNE","volume":"9","author":"van der Maaten","year":"2008","journal-title":"J. Mach. Learn. Res."},{"key":"ref46","article-title":"Self-labelling via simultaneous clustering and representation learning","author":"Asano","year":"2019","journal-title":"arXiv:1911.05371"}],"container-title":["IEEE Transactions on Information Forensics and Security"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/10206\/10810755\/10793094.pdf?arnumber=10793094","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,1,8]],"date-time":"2025-01-08T20:03:23Z","timestamp":1736366603000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10793094\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"references-count":46,"URL":"https:\/\/doi.org\/10.1109\/tifs.2024.3515809","relation":{},"ISSN":["1556-6013","1556-6021"],"issn-type":[{"value":"1556-6013","type":"print"},{"value":"1556-6021","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025]]}}}