{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,6,8]],"date-time":"2024-06-08T06:35:36Z","timestamp":1717828536486},"reference-count":68,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"name":"Jiangsu Provincial Key Laboratory of Network and Information Security","award":["BM2003201"]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans.Inform.Forensic Secur."],"published-print":{"date-parts":[[2024]]},"DOI":"10.1109\/tifs.2023.3329683","type":"journal-article","created":{"date-parts":[[2023,11,2]],"date-time":"2023-11-02T17:48:25Z","timestamp":1698947305000},"page":"1056-1070","source":"Crossref","is-referenced-by-count":4,"title":["Lightweight Radio Frequency Fingerprint Identification Scheme for V2X Based on Temporal Correlation"],"prefix":"10.1109","volume":"19","author":[{"ORCID":"http:\/\/orcid.org\/0000-0002-5304-2170","authenticated-orcid":false,"given":"Xinyu","family":"Qi","sequence":"first","affiliation":[{"name":"School of Cyber Science and Engineering, Southeast University, Nanjing, China"}]},{"ORCID":"http:\/\/orcid.org\/0000-0002-0398-4899","authenticated-orcid":false,"given":"Aiqun","family":"Hu","sequence":"additional","affiliation":[{"name":"National Mobile Communications Research Laboratory, Southeast University, Nanjing, China"}]},{"given":"Tianshu","family":"Chen","sequence":"additional","affiliation":[{"name":"National Mobile Communications Research Laboratory, Southeast University, Nanjing, China"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/JPROC.2019.2961937"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/JPROC.2019.2954595"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0155781"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/SURV.2013.041513.00174"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1016\/j.vehcom.2018.01.008"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/MCOM.2017.1600522CM"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/MWC.2010.5601959"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/TCOMM.2014.032914.120921"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/COMST.2022.3205184"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/MWC.2017.1600141WC"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/TIFS.2019.2946933"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/TVT.2022.3176496"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/TIFS.2021.3088008"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/TCCN.2022.3212414"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2022.3154595"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/TDSC.2014.2366455"},{"key":"ref17","first-page":"25","article-title":"Transient-based identification of wireless sensor nodes","volume-title":"Proc. Int. Conf. Inf. Process. Sensor Netw.","author":"Danev"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/TVT.2019.2950670"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/TIFS.2021.3106166"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/JSAC.2021.3087243"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/TCOMM.2022.3200599"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/TWC.2008.070194"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/TCOMM.2020.2967393"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/LCOMM.2023.3248127"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/LCOMM.2023.3247900"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1145\/3522783.3529520"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/JSAC.2018.2824583"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/INFOCOM.2019.8737463"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/TNSE.2021.3103805"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/TIFS.2022.3152404"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/JSTSP.2018.2796446"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1109\/INFOCOM.2019.8737597"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1109\/JSAC.2021.3087250"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2019.2911347"},{"key":"ref35","article-title":"Robustness may be at odds with accuracy","author":"Tsipras","year":"2018","journal-title":"arXiv:1805.12152"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1109\/TCOMM.2023.3268286"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1109\/tifs.2024.3360851"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1109\/GLOBECOM46510.2021.9685067"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1049\/el.2018.6229"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1016\/j.physrep.2006.11.001"},{"key":"ref41","volume-title":"LTE; Evolved Universal Terrestrial Radio Access (E-UTRA); Physical Channels and Modulation (Release 14)","year":"2017"},{"key":"ref42","article-title":"Uncovering the portability limitation of deep learning-based wireless device fingerprints","author":"Hamdaoui","year":"2022","journal-title":"arXiv:2211.07687"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1109\/CNS56114.2022.10227829"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1109\/JSTSP.2023.3239189"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1109\/TMC.2021.3064466"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1109\/TVT.2020.2971001"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1016\/j.dsp.2021.103188"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2022.3206309"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1109\/TIFS.2023.3266627"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1109\/26.870025"},{"key":"ref51","volume-title":"Technical Specification Group Radio Access Network; Evolved Universal Terrestrial Radio Access (E-UTRA); User Equipment (UE) Radio Transmission and Reception (Release 9)","year":"2010"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.1109\/VTC2022-Fall57202.2022.10012865"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1145\/3098243.3098267"},{"key":"ref54","first-page":"13271","article-title":"Exploring and exploiting hubness priors for high-quality GAN latent sampling","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Liang"},{"key":"ref55","article-title":"Explaining and harnessing adversarial examples","author":"Goodfellow","year":"2014","journal-title":"arXiv:1412.6572"},{"key":"ref56","doi-asserted-by":"publisher","DOI":"10.1109\/LCOMM.2018.2871454"},{"key":"ref57","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2022.3213892"},{"key":"ref58","article-title":"L2-constrained softmax loss for discriminative face verification","author":"Ranjan","year":"2017","journal-title":"arXiv:1703.09507"},{"key":"ref59","article-title":"An analysis of deep neural network models for practical applications","author":"Canziani","year":"2016","journal-title":"arXiv:1605.07678"},{"key":"ref60","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW53098.2021.00268"},{"key":"ref61","first-page":"9204","article-title":"Pay attention to MLPs","volume-title":"Proc. Conf. Neural Inf. Proces. Syst. (NeurIPS)","author":"Liu"},{"key":"ref62","article-title":"An empirical evaluation of generic convolutional and recurrent networks for sequence modeling","author":"Bai","year":"2018","journal-title":"arXiv:1803.01271"},{"key":"ref63","doi-asserted-by":"publisher","DOI":"10.3390\/math9233137"},{"key":"ref64","doi-asserted-by":"publisher","DOI":"10.1007\/s10618-020-00701-z"},{"key":"ref65","article-title":"TabTransformer: Tabular data modeling using contextual embeddings","author":"Huang","year":"2020","journal-title":"arXiv:2012.06678"},{"key":"ref66","article-title":"The GatedTabTransformer. An enhanced deep learning architecture for tabular modeling","author":"Cholakov","year":"2022","journal-title":"arXiv:2201.00199"},{"key":"ref67","article-title":"Deep gated recurrent and convolutional network hybrid model for univariate time series classification","author":"Elsayed","year":"2018","journal-title":"arXiv:1812.07683"},{"key":"ref68","doi-asserted-by":"publisher","DOI":"10.1016\/j.neunet.2019.04.014"}],"container-title":["IEEE Transactions on Information Forensics and Security"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/10206\/10319981\/10305173.pdf?arnumber=10305173","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,2]],"date-time":"2024-03-02T12:02:37Z","timestamp":1709380957000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10305173\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"references-count":68,"URL":"https:\/\/doi.org\/10.1109\/tifs.2023.3329683","relation":{},"ISSN":["1556-6013","1556-6021"],"issn-type":[{"value":"1556-6013","type":"print"},{"value":"1556-6021","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024]]}}}