{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,3]],"date-time":"2026-07-03T09:21:10Z","timestamp":1783070470402,"version":"3.54.6"},"reference-count":27,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100016984","name":"Xijing University","doi-asserted-by":"publisher","award":["XJ22B04"],"award-info":[{"award-number":["XJ22B04"]}],"id":[{"id":"10.13039\/501100016984","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Internet of Things"],"published-print":{"date-parts":[[2026,7]]},"DOI":"10.1016\/j.iot.2026.101967","type":"journal-article","created":{"date-parts":[[2026,5,12]],"date-time":"2026-05-12T10:10:23Z","timestamp":1778580623000},"page":"101967","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["HFL-DP NIDS: A hierarchical federated learning-based network intrusion detection method with differential privacy"],"prefix":"10.1016","volume":"38","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7338-3765","authenticated-orcid":false,"given":"Kai","family":"Yang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-0017-5897","authenticated-orcid":false,"given":"XiaoFang","family":"Dong","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"JiaRui","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"XuAn","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"LiangChen","family":"Ren","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"RuiXuan","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jun","family":"Wu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"JunHao","family":"Qiao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"issue":"10","key":"10.1016\/j.iot.2026.101967_bib0001","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.compind.2018.04.015","article-title":"The industrial internet of things (IIoT): an analysis framework","volume":"101","author":"Boyes","year":"2018","journal-title":"Comput. Ind."},{"issue":"10","key":"10.1016\/j.iot.2026.101967_bib0002","doi-asserted-by":"crossref","first-page":"3744","DOI":"10.3390\/s22103744","article-title":"Intrusion detection in internet of things systems: a review on existing approaches","volume":"22","author":"Gyamfi","year":"2022","journal-title":"Sensors"},{"issue":"1\u20132","key":"10.1016\/j.iot.2026.101967_bib0003","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1561\/2200000083","article-title":"Advances and open problems in federated learning","volume":"14","author":"Kairouz","year":"2021","journal-title":"Found. Trends Mach. Learn."},{"issue":"6","key":"10.1016\/j.iot.2026.101967_bib0004","doi-asserted-by":"crossref","first-page":"310","DOI":"10.1109\/MNET.011.2000286","article-title":"Internet of things intrusion detection: centralized, on-device, or federated learning?","volume":"34","author":"Rahman","year":"2020","journal-title":"IEEE Netw."},{"key":"10.1016\/j.iot.2026.101967_bib0005","series-title":"Proc. IEEE ICC 2020","first-page":"1","article-title":"Client-edge-cloud hierarchical federated learning","author":"Liu","year":"2020"},{"key":"10.1016\/j.iot.2026.101967_bib0006","doi-asserted-by":"crossref","first-page":"406","DOI":"10.1016\/j.future.2021.10.016","article-title":"Communication-efficient hierarchical federated learning for IoT heterogeneous systems with imbalanced data","volume":"128","author":"Abdellatif","year":"2022","journal-title":"Future Gener. Comput. Syst."},{"issue":"1","key":"10.1016\/j.iot.2026.101967_bib0007","doi-asserted-by":"crossref","DOI":"10.1145\/3687124","article-title":"Survey on federated learning for intrusion detection system: concept, architectures, aggregation strategies, challenges, and future directions","volume":"57","author":"Khraisat","year":"2024","journal-title":"ACM Comput. Surv."},{"issue":"2","key":"10.1016\/j.iot.2026.101967_bib0008","doi-asserted-by":"crossref","first-page":"450","DOI":"10.3390\/s22020450","article-title":"Federated learning in edge computing: a systematic survey","volume":"22","author":"Abreha","year":"2022","journal-title":"Sensors"},{"issue":"3","key":"10.1016\/j.iot.2026.101967_bib0009","doi-asserted-by":"crossref","first-page":"2031","DOI":"10.1109\/COMST.2020.2986024","article-title":"Federated learning in mobile edge networks: a comprehensive survey","volume":"22","author":"Lim","year":"2020","journal-title":"IEEE Commun. Surv. Tutor."},{"key":"10.1016\/j.iot.2026.101967_sbref0010","series-title":"Proc. MLSys 2020","article-title":"Federated optimization in heterogeneous networks","author":"Li","year":"2020"},{"key":"10.1016\/j.iot.2026.101967_bib0011","unstructured":"S.P. Karimireddy, S. Kale, M. Mohri, S. Reddi, S.U. Stich, A.T. Suresh, SCAFFOLD: stochastic controlled averaging for federated learning, (2020), 10.48550\/arXiv.1910.06378."},{"key":"10.1016\/j.iot.2026.101967_bib0012","series-title":"2018 IEEE 31st Computer Security Foundations Symposium (CSF)","article-title":"Privacy risk in machine learning: analyzing the connection to overfitting","author":"Yeom","year":"2018"},{"key":"10.1016\/j.iot.2026.101967_bib0013","series-title":"2019 IEEE Symposium on Security and Privacy (SP)","article-title":"Comprehensive privacy analysis of deep learning: passive and active white-box inference attacks against centralized and federated learning","author":"Nasr","year":"2019"},{"key":"10.1016\/j.iot.2026.101967_bib0014","series-title":"2019 IEEE Symposium on Security and Privacy (SP)","first-page":"691","article-title":"Exploiting unintended feature leakage in collaborative learning","author":"Melis","year":"2019"},{"key":"10.1016\/j.iot.2026.101967_bib0015","series-title":"Technical Report","article-title":"Guidelines for Evaluating Differential Privacy Guarantees","author":"Near","year":"2025"},{"key":"10.1016\/j.iot.2026.101967_bib0016","unstructured":"H.B. McMahan, G. Andrew, \u00da. Erlingsson, S. Chien, I. Mironov, N. Papernot, P. Kairouz, A general approach to adding differential privacy to iterative training procedures, (2018), 10.48550\/arXiv.1812.06210."},{"key":"10.1016\/j.iot.2026.101967_bib0017","unstructured":"I. Mironov, K. Talwar, L. Zhang, R\u00e9nyi Differential Privacy of the Sampled Gaussian Mechanism, (2019), 10.48550\/arXiv.1908.10530."},{"key":"10.1016\/j.iot.2026.101967_bib0018","doi-asserted-by":"crossref","DOI":"10.1016\/j.compeleceng.2022.108379","article-title":"HBFL: a hierarchical blockchain-based federated learning framework for collaborative IoT intrusion detection","volume":"103","author":"Sarhan","year":"2022","journal-title":"Comput. Electr. Eng."},{"key":"10.1016\/j.iot.2026.101967_bib0019","doi-asserted-by":"crossref","first-page":"299","DOI":"10.1016\/j.comcom.2022.06.015","article-title":"FEDGAN-IDS: privacy-preserving IDS using GAN and federated learning","volume":"192","author":"Tabassum","year":"2022","journal-title":"Comput. Commun."},{"key":"10.1016\/j.iot.2026.101967_bib0020","doi-asserted-by":"crossref","DOI":"10.1016\/j.adhoc.2024.103540","article-title":"A federated learning-based novel intrusion detection system for zero trust internet of things","volume":"162","author":"Javeed","year":"2024","journal-title":"Ad Hoc Netw."},{"key":"10.1016\/j.iot.2026.101967_bib0021","doi-asserted-by":"crossref","DOI":"10.1016\/j.iot.2025.101820","article-title":"Combining epsilon-greedy reinforcement learning based gradient sparsification and siamese neural networks for few-shot federated TinyML intrusion detection in IoT","volume":"34","author":"Fusco","year":"2025","journal-title":"Internet Things"},{"key":"10.1016\/j.iot.2026.101967_bib0022","doi-asserted-by":"crossref","DOI":"10.1016\/j.cose.2023.103299","article-title":"Clustered federated learning architecture for network anomaly detection in large-scale heterogeneous IoT networks","volume":"131","author":"S\u00e1ez-de C\u00e1mara","year":"2023","journal-title":"Comput. Secur."},{"key":"10.1016\/j.iot.2026.101967_bib0023","article-title":"A robust multi-stage intrusion detection system for in-vehicle network security using hierarchical federated learning","volume":"49","author":"Althunayyan","year":"2024","journal-title":"Veh. Commun."},{"issue":"23","key":"10.1016\/j.iot.2026.101967_bib0024","doi-asserted-by":"crossref","first-page":"7296","DOI":"10.3390\/s25237296","article-title":"Privacy-preserving hierarchical fog federated learning (PP-HFFL) for IoT intrusion detection","volume":"25","author":"Islam","year":"2025","journal-title":"Sensors"},{"issue":"7","key":"10.1016\/j.iot.2026.101967_bib0025","doi-asserted-by":"crossref","first-page":"3741","DOI":"10.3934\/era.2023190","article-title":"Hierarchical federated learning with global differential privacy","volume":"31","author":"Long","year":"2023","journal-title":"Electron. Res. Arch."},{"key":"10.1016\/j.iot.2026.101967_bib0026","doi-asserted-by":"crossref","first-page":"3454","DOI":"10.1109\/TIFS.2020.2988575","article-title":"Federated learning with differential privacy: algorithms and performance analysis","volume":"15","author":"Wei","year":"2020","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"issue":"1","key":"10.1016\/j.iot.2026.101967_bib0027","doi-asserted-by":"crossref","first-page":"10","DOI":"10.3390\/jcp6010010","article-title":"FedPrIDS: privacy-preserving federated learning for collaborative network intrusion detection in IoT","volume":"6","author":"Mankotia","year":"2026","journal-title":"J. Cybersecur. Priv."}],"container-title":["Internet of Things"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S2542660526000971?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S2542660526000971?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,7,3]],"date-time":"2026-07-03T09:08:54Z","timestamp":1783069734000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S2542660526000971"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7]]},"references-count":27,"alternative-id":["S2542660526000971"],"URL":"https:\/\/doi.org\/10.1016\/j.iot.2026.101967","relation":{},"ISSN":["2542-6605"],"issn-type":[{"value":"2542-6605","type":"print"}],"subject":[],"published":{"date-parts":[[2026,7]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"HFL-DP NIDS: A hierarchical federated learning-based network intrusion detection method with differential privacy","name":"articletitle","label":"Article Title"},{"value":"Internet of Things","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.iot.2026.101967","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"101967"}}