{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,21]],"date-time":"2026-05-21T14:35:54Z","timestamp":1779374154654,"version":"3.53.1"},"reference-count":29,"publisher":"Springer Science and Business Media LLC","issue":"13","license":[{"start":{"date-parts":[[2025,8,29]],"date-time":"2025-08-29T00:00:00Z","timestamp":1756425600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,8,29]],"date-time":"2025-08-29T00:00:00Z","timestamp":1756425600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"TIH (Technology Innovation Hub), Indian Institute of Technology, Bhilai, for funding this research under the entrepreneurship in residence (EIR) project.","award":["Project No. 737"],"award-info":[{"award-number":["Project No. 737"]}]},{"name":"TIH (Technology Innovation Hub), Indian Institute of Technology, Bhilai, for funding this research under the entrepreneurship in residence (EIR) project.","award":["Project No. 737"],"award-info":[{"award-number":["Project No. 737"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Supercomput"],"DOI":"10.1007\/s11227-025-07774-x","type":"journal-article","created":{"date-parts":[[2025,8,29]],"date-time":"2025-08-29T19:52:37Z","timestamp":1756497157000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["FedShield: federated learning based robust online payment fraud detection"],"prefix":"10.1007","volume":"81","author":[{"given":"Narendra","family":"Singh","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Somanath","family":"Tripathy","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,8,29]]},"reference":[{"issue":"1","key":"7774_CR1","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.intmar.2014.10.001","volume":"30","author":"P Hille","year":"2015","unstructured":"Hille P, Walsh G, Cleveland M (2015) Consumer fear of online identity theft: scale development and validation. J Interact Mark 30(1):1\u201319","journal-title":"J Interact Mark"},{"key":"7774_CR2","doi-asserted-by":"publisher","first-page":"753","DOI":"10.1007\/s12652-015-0276-9","volume":"6","author":"L Coppolino","year":"2015","unstructured":"Coppolino L, D\u2019Antonio S, Formicola V, Massei C, Romano L (2015) Use of the dempster-shafer theory to detect account takeovers in mobile money transfer services. J Ambient Intell Humaniz Comput 6:753\u2013762","journal-title":"J Ambient Intell Humaniz Comput"},{"key":"7774_CR3","doi-asserted-by":"crossref","unstructured":"Thomas K, Li F, Grier C, Paxson V (2014) Consequences of connectivity characterizing account hijacking on twitter In: Proceedings of the 2014 ACM SIGSAC Conference on Computer and Communications Security. pp. 489\u2013500","DOI":"10.1145\/2660267.2660282"},{"key":"7774_CR4","doi-asserted-by":"publisher","DOI":"10.1016\/j.jbankfin.2023.106854","volume":"152","author":"A Horvath","year":"2023","unstructured":"Horvath A, Kay B, Wix C (2023) The covid-19 shock and consumer credit: Evidence from credit card data. J Bank Financ 152:106854","journal-title":"J Bank Financ"},{"issue":"1","key":"7774_CR5","doi-asserted-by":"publisher","first-page":"301","DOI":"10.1109\/TDSC.2020.2991872","volume":"19","author":"C Wang","year":"2020","unstructured":"Wang C, Zhu H (2020) Representing fine-grained co-occurrences for behavior-based fraud detection in online payment services. IEEE Trans Depend Secure Comput 19(1):301\u2013315","journal-title":"IEEE Trans Depend Secure Comput"},{"key":"7774_CR6","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2023.119562","volume":"217","author":"H Fanai","year":"2023","unstructured":"Fanai H, Abbasimehr H (2023) A novel combined approach based on deep autoencoder and deep classifiers for credit card fraud detection. Expert Syst Appl 217:119562","journal-title":"Expert Syst Appl"},{"key":"7774_CR7","doi-asserted-by":"crossref","unstructured":"Bestami Yuksel B, Bahtiyar S, Yilmazer A (2020) Credit card fraud detection with nca dimensionality reduction In: 13th International Conference on Security of Information and Networks. pp. 1\u20137","DOI":"10.1145\/3433174.3433178"},{"key":"7774_CR8","doi-asserted-by":"crossref","unstructured":"Yang W, Zhang Y, Ye K, Li L, Xu C-Z (2019) Ffd: a federated learning based method for credit card fraud detection. In: Big Data\u2013BigData 2019: 8th International Congress, Held As Part of the Services Conference Federation. SCF 2019, San Diego, CA, USA, June 25\u201330, 2019, Proceedings 8, pp. 18\u201332. Springer","DOI":"10.1007\/978-3-030-23551-2_2"},{"key":"7774_CR9","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2024.124979","volume":"256","author":"Y Tang","year":"2024","unstructured":"Tang Y, Liang Y (2024) Credit card fraud detection based on federated graph learning. Expert Syst Appl 256:124979","journal-title":"Expert Syst Appl"},{"key":"7774_CR10","first-page":"1950","volume":"18","author":"X Zhang","year":"2022","unstructured":"Zhang X, Wang Y, Chen Z (2022) Blockchain and federated learning for privacy-preserving fraud detection. IEEE Trans Ind Inf 18:1950\u20131960","journal-title":"IEEE Trans Ind Inf"},{"key":"7774_CR11","doi-asserted-by":"crossref","unstructured":"Aurna NF, Hossain MD, Taenaka Y, Kadobayashi Y (2023) Federated learning-based credit card fraud detection: Performance analysis with sampling methods and deep learning algorithms. In: 2023 IEEE International Conference on Cyber Security and Resilience (CSR), pp. 180\u2013186. IEEE","DOI":"10.1109\/CSR57506.2023.10224978"},{"key":"7774_CR12","doi-asserted-by":"publisher","first-page":"381","DOI":"10.1016\/j.eswa.2018.06.011","volume":"110","author":"S Nami","year":"2018","unstructured":"Nami S, Shajari M (2018) Cost-sensitive payment card fraud detection based on dynamic random forest and k-nearest neighbors. Expert Syst Appl 110:381\u2013392","journal-title":"Expert Syst Appl"},{"issue":"12","key":"7774_CR13","doi-asserted-by":"publisher","first-page":"14571","DOI":"10.1007\/s11227-022-04465-9","volume":"78","author":"G Zioviris","year":"2022","unstructured":"Zioviris G, Kolomvatsos K, Stamoulis G (2022) Credit card fraud detection using a deep learning multistage model. J Supercomput 78(12):14571\u201314596","journal-title":"J Supercomput"},{"issue":"4","key":"7774_CR14","doi-asserted-by":"publisher","first-page":"537","DOI":"10.1007\/s11227-025-06983-8","volume":"81","author":"S Khosravi","year":"2025","unstructured":"Khosravi S, Kargari M, Teimourpour B, Talebi M (2025) Transaction fraud detection via attentional spatial-temporal GNN. J Supercomput 81(4):537","journal-title":"J Supercomput"},{"key":"7774_CR15","doi-asserted-by":"publisher","first-page":"151","DOI":"10.1186\/s40537-021-00541-8","volume":"8","author":"I Benchaji","year":"2021","unstructured":"Benchaji I, Douzi S, Ouahidi B, Jaafari J (2021) Enhanced credit card fraud detection based on attention mechanism and LSTM deep model. J Big Data 8:151","journal-title":"J Big Data"},{"key":"7774_CR16","doi-asserted-by":"crossref","unstructured":"Jarovsky A, Milo T, Novgorodov S, Tan W (2018) Rule sharing for fraud detection via adaptation In: Proc. IEEE 34th International Conference on Data Engineering. pp. 125\u2013136","DOI":"10.1109\/ICDE.2018.00021"},{"key":"7774_CR17","unstructured":"Milo T, Novgorodov S, Tan W (2018) Interactive rule refinement for fraud detection In: Proc. 21th International Conference on Extending Database Technology, pp. 265\u2013276"},{"key":"7774_CR18","first-page":"21","volume":"72","author":"AGC Sa","year":"2018","unstructured":"Sa AGC, Pereira ACM, Pappa GL (2018) A customized classification algorithm for credit card fraud detection. Eng Appl AI 72:21\u201329","journal-title":"Eng Appl AI"},{"key":"7774_CR19","doi-asserted-by":"crossref","unstructured":"Zheng P, Yuan S, Wu X (2019) Safe: A neural survival analysis model for fraud early detection. In: Proceedings of the AAAI Conference on Artificial Intelligence. pp. 1278\u20131285","DOI":"10.1609\/aaai.v33i01.33011278"},{"key":"7774_CR20","doi-asserted-by":"crossref","unstructured":"West J, Bhattacharya M, Islam MR (2014) Intelligent financial fraud detection practices: An investigation. In: Proceedings of International Conference on Security Privacy Communication in Networks. pp. 186\u2013203","DOI":"10.1007\/978-3-319-23802-9_16"},{"key":"7774_CR21","doi-asserted-by":"publisher","first-page":"6840","DOI":"10.1109\/TIFS.2024.3424301","volume":"19","author":"J Huang","year":"2024","unstructured":"Huang J, Bai J-X, Zhang X, Liu Z, Feng Y, Liu J, Sun X, Dong M, Li M (2024) Keystrokesniffer: An off-the-shelf smartphone can eavesdrop on your privacy from anywhere. IEEE Trans Inf Forensics Secur 19:6840\u20136855","journal-title":"IEEE Trans Inf Forensics Secur"},{"issue":"7","key":"7774_CR22","doi-asserted-by":"publisher","first-page":"7753","DOI":"10.1109\/TMC.2023.3338954","volume":"23","author":"J Huang","year":"2023","unstructured":"Huang J, Liu B, Miao C, Zhang X, Liu J, Su L, Liu Z, Gu Y (2023) Phyfinatt: An undetectable attack framework against phy layer fingerprint-based wifi authentication. IEEE Trans Mob Comput 23(7):7753\u20137770","journal-title":"IEEE Trans Mob Comput"},{"issue":"6","key":"7774_CR23","doi-asserted-by":"publisher","first-page":"395","DOI":"10.1007\/s10586-024-05075-1","volume":"28","author":"H-H Huynh","year":"2025","unstructured":"Huynh H-H, Nguyen X-H, Nguyen X-D, Le K-H (2025) Bigsids: an efficient sdn-based network intrusion detection systems for big data environments. Clust Comput 28(6):395","journal-title":"Clust Comput"},{"key":"7774_CR24","doi-asserted-by":"crossref","unstructured":"Nguyen V-T, Hoang V-C, Nguyen X-H, Le K-H (2022) Towards a high-performance threat-aware system for software-defined networks. In: 2022 International Conference on Advanced Technologies for Communications (ATC), pp. 280\u2013285. IEEE","DOI":"10.1109\/ATC55345.2022.9942972"},{"issue":"3","key":"7774_CR25","doi-asserted-by":"publisher","first-page":"2565","DOI":"10.1109\/TDSC.2022.3186733","volume":"20","author":"C Wang","year":"2022","unstructured":"Wang C, Chai S, Zhu H, Jiang C (2022) Caesar: an online payment anti-fraud integration system with decision explainability. IEEE Trans Dependable Secure Comput 20(3):2565\u20132577","journal-title":"IEEE Trans Dependable Secure Comput"},{"key":"7774_CR26","doi-asserted-by":"crossref","unstructured":"Zhang Y, Xiang T, Hospedales TM, Lu H (2018) Deep mutual learning In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4320\u20134328","DOI":"10.1109\/CVPR.2018.00454"},{"issue":"3\u20134","key":"7774_CR27","first-page":"211","volume":"9","author":"C Dwork","year":"2014","unstructured":"Dwork C, Roth A et al (2014) The algorithmic foundations of differential privacy. Found Trends Theor Comput Sci 9(3\u20134):211\u2013407","journal-title":"Found Trends Theor Comput Sci"},{"key":"7774_CR28","unstructured":"Yin D, Chen Y, Kannan R, Bartlett P (2018) Byzantine-robust distributed learning: towards optimal statistical rates. In: International Conference on Machine Learning, pp. 5650\u20135659"},{"key":"7774_CR29","doi-asserted-by":"crossref","unstructured":"Graham RL, Woodall TS, Squyres JM (2006) Open MPI: A flexible high performance MPI. In: Parallel Processing and Applied Mathematics: 6th International Conference, PPAM 2005, Pozna\u0144, Poland, September 11\u201314, 2005, Revised Selected Papers 6, pp. 228\u2013239. Springer","DOI":"10.1007\/11752578_29"}],"container-title":["The Journal of Supercomputing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11227-025-07774-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11227-025-07774-x\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11227-025-07774-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,9]],"date-time":"2025-09-09T21:53:47Z","timestamp":1757454827000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11227-025-07774-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,8,29]]},"references-count":29,"journal-issue":{"issue":"13","published-online":{"date-parts":[[2025,8]]}},"alternative-id":["7774"],"URL":"https:\/\/doi.org\/10.1007\/s11227-025-07774-x","relation":{},"ISSN":["1573-0484"],"issn-type":[{"value":"1573-0484","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,8,29]]},"assertion":[{"value":"20 May 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"14 August 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"29 August 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no Conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"1289"}}