{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,11]],"date-time":"2026-02-11T21:46:04Z","timestamp":1770846364831,"version":"3.50.1"},"reference-count":24,"publisher":"IEEE","license":[{"start":{"date-parts":[[2025,10,5]],"date-time":"2025-10-05T00:00:00Z","timestamp":1759622400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2025,10,5]],"date-time":"2025-10-05T00:00:00Z","timestamp":1759622400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,10,5]]},"DOI":"10.1109\/smc58881.2025.11343314","type":"proceedings-article","created":{"date-parts":[[2026,1,28]],"date-time":"2026-01-28T20:54:44Z","timestamp":1769633684000},"page":"6816-6823","source":"Crossref","is-referenced-by-count":0,"title":["FLIFRA: Hybrid Data Poisoning Attack Detection in Federated Learning for IoT Security"],"prefix":"10.1109","author":[{"given":"Mulualem Bitew","family":"Anley","sequence":"first","affiliation":[{"name":"Università Degli Studi di Milano,Department of Computer Science,Italy"}]},{"given":"Angelo","family":"Genovese","sequence":"additional","affiliation":[{"name":"Università Degli Studi di Milano,Department of Computer Science,Italy"}]},{"given":"Tibebe Beshah","family":"Tesema","sequence":"additional","affiliation":[{"name":"Addis Ababa University,School of Information Science,Ethiopia"}]},{"given":"Vincenzo","family":"Piuri","sequence":"additional","affiliation":[{"name":"Università Degli Studi di Milano,Department of Computer Science,Italy"}]}],"member":"263","reference":[{"key":"ref1","article-title":"Communication-efficient learning of deep networks from decentralized data","volume-title":"Proc. of AIS","author":"McMahan"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-43415-0_24"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1016\/j.neunet.2023.11.019"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/ICDCS60910.2024.00139"},{"key":"ref5","article-title":"Local model poisoning attacks to byzantine-robust federated learning","volume-title":"Proc. of USENIX","author":"Fang"},{"key":"ref6","article-title":"FL-WBC: Enhancing robustness against model poisoning attacks in federated learning from a client perspective","volume-title":"Proc. of NIPS","author":"Sun"},{"key":"ref7","article-title":"Byzantine-robust distributed learning: Towards optimal statistical rates","volume-title":"Proc. of ICML","author":"Yin"},{"key":"ref8","article-title":"Secure federated learning against model poisoning attacks via client filtering","author":"Yaldiz","year":"2023"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1186\/s42400-021-00077-7"},{"key":"ref10","article-title":"Machine learning with adversaries: Byzantine tolerant gradient descent","volume-title":"Proc. of NIPS","volume":"30","author":"Blanchard"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58951-6_24"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1145\/3605098.3635896"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1016\/j.inffus.2022.09.011"},{"key":"ref14","article-title":"Dual defense: Enhancing privacy and mitigating poisoning attacks in federated learning","volume-title":"Proc. of NIPS","volume":"37","author":"Xu"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.14722\/ndss.2024.24620"},{"key":"ref16","article-title":"Can you really backdoor federated learning?","author":"Sun","year":"2019"},{"key":"ref17","article-title":"FLAME: Taming backdoors in federated learning","volume-title":"Proc. of USENIX","author":"Nguyen"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1016\/j.jisa.2025.104275"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1016\/j.jisa.2024.103916"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.5220\/0006639801080116"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1016\/j.future.2019.05.041"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1016\/j.cose.2024.103962"},{"key":"ref23","article-title":"Measuring the effects of non-identical data distribution for federated visual classification","author":"Hsu","year":"2019"},{"key":"ref24","article-title":"How to backdoor federated learning","volume-title":"Proc. of AIS","author":"Bagdasaryan"}],"event":{"name":"2025 IEEE International Conference on Systems, Man, and Cybernetics (SMC)","location":"Vienna, Austria","start":{"date-parts":[[2025,10,5]]},"end":{"date-parts":[[2025,10,8]]}},"container-title":["2025 IEEE International Conference on Systems, Man, and Cybernetics (SMC)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/11342430\/11342431\/11343314.pdf?arnumber=11343314","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,11]],"date-time":"2026-02-11T20:53:16Z","timestamp":1770843196000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/11343314\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,5]]},"references-count":24,"URL":"https:\/\/doi.org\/10.1109\/smc58881.2025.11343314","relation":{},"subject":[],"published":{"date-parts":[[2025,10,5]]}}}