{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T10:25:00Z","timestamp":1773829500924,"version":"3.50.1"},"reference-count":44,"publisher":"Institution of Engineering and Technology (IET)","issue":"1","license":[{"start":{"date-parts":[[2025,9,15]],"date-time":"2025-09-15T00:00:00Z","timestamp":1757894400000},"content-version":"vor","delay-in-days":257,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62401070"],"award-info":[{"award-number":["62401070"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004731","name":"Natural Science Foundation of Zhejiang Province","doi-asserted-by":"publisher","award":["LQ24F030023"],"award-info":[{"award-number":["LQ24F030023"]}],"id":[{"id":"10.13039\/501100004731","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["ietresearch.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["IET Information Security"],"published-print":{"date-parts":[[2025,1]]},"abstract":"<jats:p>The rapid proliferation of Internet of Things (IoT) devices has revolutionized various industries by enabling smart grids, smart cities, and other applications that rely on seamless connectivity and real\u2010time data processing. However, this growth has also introduced significant security challenges due to the scale, heterogeneity, and resource constraints of IoT systems. Traditional intrusion detection systems (IDS) often struggle to address these challenges effectively, as they require centralized data collection and processing, which raises concerns about data privacy, communication overhead, and scalability. To address these issues, this paper investigates the application of federated learning for network intrusion detection in IoT environments. We first evaluate a range of machine learning (ML) and deep learning (DL) models, finding that the random forest model achieves the highest classification accuracy. We then propose a federated learning approach that allows distributed IoT devices to collaboratively train ML models without sharing raw data, thereby preserving privacy and reducing communication costs. Experimental results using the UNSW\u2010NB15 dataset demonstrate that this approach achieves promising outcomes in the IoT context, with minimal performance degradation compared to centralized learning. Our findings highlight the potential of federated learning as an effective, decentralized solution for network intrusion detection in IoT environments, addressing critical challenges, such as data privacy, heterogeneity, and scalability.<\/jats:p>","DOI":"10.1049\/ise2\/8432654","type":"journal-article","created":{"date-parts":[[2025,9,15]],"date-time":"2025-09-15T12:36:00Z","timestamp":1757939760000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Enhancing IoT Security via Federated Learning: A Comprehensive Approach to Intrusion Detection"],"prefix":"10.1049","volume":"2025","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-2993-7523","authenticated-orcid":false,"given":"Ye","family":"Bai","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0953-5047","authenticated-orcid":false,"given":"Weiwei","family":"Jiang","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0007-6436-3950","authenticated-orcid":false,"given":"Jianbin","family":"Mu","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0008-3428-292X","authenticated-orcid":false,"given":"Shang","family":"Liu","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0005-5986-5716","authenticated-orcid":false,"given":"Weixi","family":"Gu","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0008-0497-9793","authenticated-orcid":false,"given":"Shuke","family":"Wang","sequence":"additional","affiliation":[]}],"member":"265","published-online":{"date-parts":[[2025,9,15]]},"reference":[{"key":"e_1_2_10_1_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.comcom.2024.108000"},{"key":"e_1_2_10_2_2","doi-asserted-by":"publisher","DOI":"10.3390\/s25041039"},{"key":"e_1_2_10_3_2","doi-asserted-by":"publisher","DOI":"10.1109\/TCE.2024.3361037"},{"key":"e_1_2_10_4_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jiixd.2023.12.001"},{"key":"e_1_2_10_5_2","doi-asserted-by":"publisher","DOI":"10.48130\/DTS-2023-0017"},{"key":"e_1_2_10_6_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2023.122774"},{"key":"e_1_2_10_7_2","doi-asserted-by":"publisher","DOI":"10.1155\/int\/3715086"},{"key":"e_1_2_10_8_2","doi-asserted-by":"publisher","DOI":"10.1109\/JSTARS.2024.3415729"},{"key":"e_1_2_10_9_2","doi-asserted-by":"publisher","DOI":"10.1109\/LWC.2024.3457920"},{"key":"e_1_2_10_10_2","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2024.3446640"},{"key":"e_1_2_10_11_2","doi-asserted-by":"publisher","DOI":"10.26599\/TST.2023.9010128"},{"key":"e_1_2_10_12_2","doi-asserted-by":"publisher","DOI":"10.1109\/TCCN.2024.3510562"},{"key":"e_1_2_10_13_2","doi-asserted-by":"publisher","DOI":"10.1109\/TCE.2024.3476156"},{"key":"e_1_2_10_14_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.inffus.2024.102682"},{"key":"e_1_2_10_15_2","doi-asserted-by":"publisher","DOI":"10.1002\/spe.3386"},{"key":"e_1_2_10_16_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.inffus.2023.102141"},{"key":"e_1_2_10_17_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-024-70032-2"},{"key":"e_1_2_10_18_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11036-024-02435-4"},{"key":"e_1_2_10_19_2","doi-asserted-by":"crossref","unstructured":"MoustafaN.andSlayJ. UNSW-NB15: A Comprehensive Data Set for Network Intrusion Detection Systems (UNSW-NB15 Network Data Set) 2015 Military Communications and Information Systems Conference (MilCIS) 2015 IEEE 1\u20136.","DOI":"10.1109\/MilCIS.2015.7348942"},{"key":"e_1_2_10_20_2","doi-asserted-by":"publisher","DOI":"10.26599\/TST.2023.9010158"},{"key":"e_1_2_10_21_2","doi-asserted-by":"publisher","DOI":"10.1002\/ett.4150"},{"key":"e_1_2_10_22_2","doi-asserted-by":"publisher","DOI":"10.1007\/s44285-024-00020-5"},{"key":"e_1_2_10_23_2","doi-asserted-by":"crossref","first-page":"108","DOI":"10.47852\/bonviewJCCE42023249","article-title":"A Systematic Analysis and Review on Intrusion Detection Systems Using Machine Learning and Deep Learning Algorithms","volume":"4","author":"Jacob S. L.","year":"2022","journal-title":"Journal of Computational and Cognitive Engineering"},{"key":"e_1_2_10_24_2","doi-asserted-by":"publisher","DOI":"10.1002\/eng2.12697"},{"key":"e_1_2_10_25_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.cosrev.2020.100317"},{"key":"e_1_2_10_26_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.iotcps.2023.09.003"},{"key":"e_1_2_10_27_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.comnet.2021.108661"},{"key":"e_1_2_10_28_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.comcom.2022.09.012"},{"key":"e_1_2_10_29_2","doi-asserted-by":"publisher","DOI":"10.4108\/eai.6-10-2021.171247"},{"key":"e_1_2_10_30_2","doi-asserted-by":"publisher","DOI":"10.26599\/BDMA.2022.9020038"},{"key":"e_1_2_10_31_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.future.2020.02.051"},{"key":"e_1_2_10_32_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.teler.2022.100009"},{"key":"e_1_2_10_33_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.future.2019.03.043"},{"key":"e_1_2_10_34_2","article-title":"An Efficient Neuro Deep Learning Intrusion Detection System for Mobile Adhoc Networks","volume":"9","author":"Venkateswaran N.","year":"2022","journal-title":"EAI Endorsed Transactions on Scalable Information Systems"},{"key":"e_1_2_10_35_2","doi-asserted-by":"publisher","DOI":"10.26599\/BDMA.2020.9020003"},{"key":"e_1_2_10_36_2","doi-asserted-by":"publisher","DOI":"10.4108\/eetsis.5445"},{"key":"e_1_2_10_37_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.dsm.2024.05.002"},{"key":"e_1_2_10_38_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.teler.2022.100030"},{"key":"e_1_2_10_39_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.future.2022.01.026"},{"key":"e_1_2_10_40_2","doi-asserted-by":"crossref","first-page":"223","DOI":"10.47852\/bonviewJCCE42023751","article-title":"An Advanced Cyber Security Model Using Federated Machine Learning Approach for Intrusion Detection in Networks","volume":"4","author":"Laddi M.","year":"2022","journal-title":"Journal of Computational and Cognitive Engineering"},{"key":"e_1_2_10_41_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2023.121000"},{"key":"e_1_2_10_42_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.comcom.2022.06.015"},{"key":"e_1_2_10_43_2","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2021.3088938"},{"key":"e_1_2_10_44_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.cose.2023.103106"}],"container-title":["IET Information Security"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/ietresearch.onlinelibrary.wiley.com\/doi\/pdf\/10.1049\/ise2\/8432654","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/ietresearch.onlinelibrary.wiley.com\/doi\/full-xml\/10.1049\/ise2\/8432654","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/ietresearch.onlinelibrary.wiley.com\/doi\/pdf\/10.1049\/ise2\/8432654","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,8]],"date-time":"2026-03-08T22:32:50Z","timestamp":1773009170000},"score":1,"resource":{"primary":{"URL":"https:\/\/ietresearch.onlinelibrary.wiley.com\/doi\/10.1049\/ise2\/8432654"}},"subtitle":[],"editor":[{"given":"Jiwei","family":"Tian","sequence":"additional","affiliation":[]}],"short-title":[],"issued":{"date-parts":[[2025,1]]},"references-count":44,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2025,1]]}},"alternative-id":["10.1049\/ise2\/8432654"],"URL":"https:\/\/doi.org\/10.1049\/ise2\/8432654","archive":["Portico"],"relation":{},"ISSN":["1751-8709","1751-8717"],"issn-type":[{"value":"1751-8709","type":"print"},{"value":"1751-8717","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,1]]},"assertion":[{"value":"2025-05-20","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-07-26","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-09-15","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}],"article-number":"8432654"}}