{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,3]],"date-time":"2025-11-03T18:06:23Z","timestamp":1762193183723,"version":"build-2065373602"},"reference-count":52,"publisher":"Springer Science and Business Media LLC","issue":"13","license":[{"start":{"date-parts":[[2025,9,19]],"date-time":"2025-09-19T00:00:00Z","timestamp":1758240000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,9,19]],"date-time":"2025-09-19T00:00:00Z","timestamp":1758240000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Cluster Comput"],"published-print":{"date-parts":[[2025,11]]},"DOI":"10.1007\/s10586-025-05587-4","type":"journal-article","created":{"date-parts":[[2025,9,19]],"date-time":"2025-09-19T17:00:02Z","timestamp":1758301202000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["HFEL: A hybrid federated ensemble learning framework for intrusion detection in IoT networks"],"prefix":"10.1007","volume":"28","author":[{"given":"Salah El","family":"Hajla","sequence":"first","affiliation":[]},{"given":"El Mahfoud","family":"Ennaji","sequence":"additional","affiliation":[]},{"given":"Yassine","family":"Maleh","sequence":"additional","affiliation":[]},{"given":"Soufyane","family":"Mounir","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,9,19]]},"reference":[{"key":"5587_CR1","doi-asserted-by":"publisher","first-page":"108040","DOI":"10.1016\/j.comnet.2021.108040","volume":"192","author":"L Babun","year":"2021","unstructured":"Babun, L., Denney, K., Celik, Z.B., McDaniel, P., Uluagac, S.: A survey on IoT platforms: Communication, security, and privacy perspectives. Comput. Netw. 192, 108040 (2021). https:\/\/doi.org\/10.1016\/j.comnet.2021.108040","journal-title":"Comput. Netw."},{"key":"5587_CR2","doi-asserted-by":"publisher","unstructured":"De Oliveira, G.W., Nogueira, M., dos Santos, A.L., Batista, D.M.: \u2018Intelligent VNF Placement to Mitigate DDoS Attacks on Industrial IoT\u2019, IEEE Trans Netw Serv Manag.\u00a020(2), 1319\u20131331 (2023). https:\/\/doi.org\/10.1109\/TNSM.2023.3274364","DOI":"10.1109\/TNSM.2023.3274364"},{"key":"5587_CR3","doi-asserted-by":"publisher","unstructured":"Siwakoti, Y.R., Bhurtel, M., Rawat, D.B., Oest, A., Johnson, R.C.: \u2018Advances in IoT Security: Vulnerabilities, Enabled Criminal Services, Attacks, and Countermeasures\u2019, IEEE Internet Things J.\u00a010(13), 11224\u201311239, (2023). https:\/\/doi.org\/10.1109\/JIOT.2023.3252594","DOI":"10.1109\/JIOT.2023.3252594"},{"key":"5587_CR4","doi-asserted-by":"publisher","unstructured":"Jabraeil Jamali, M., Heidari, A., Norouzi, F., Bahrami, B., Allahverdizadeh, P.: Towards the Internet of Things: Architectures, Security, and Applications. (2019). https:\/\/doi.org\/10.1007\/978-3-030-18468-1","DOI":"10.1007\/978-3-030-18468-1"},{"issue":"3","key":"5587_CR5","doi-asserted-by":"publisher","first-page":"1646","DOI":"10.1109\/COMST.2020.2988293","volume":"22","author":"MA Al-Garadi","year":"2020","unstructured":"Al-Garadi, M.A., Mohamed, A., Al-Ali, A.K., Du, X., Ali, I., Guizani, M.: A survey of machine and deep learning methods for internet of things (IoT) security. IEEE Commun. Surv. Tutor. 22(3), 1646\u20131685 (2020). https:\/\/doi.org\/10.1109\/COMST.2020.2988293","journal-title":"IEEE Commun. Surv. Tutor."},{"key":"5587_CR6","doi-asserted-by":"publisher","unstructured":"El Mahfoud, E., El Hajla, S., Yassine, M., Mounir, S.: Machine Learning Algorithms for Intrusion Detection in IoT Prediction and Performance Analysis, 236 (2024). https:\/\/doi.org\/10.1016\/j.procs.2024.05.054","DOI":"10.1016\/j.procs.2024.05.054"},{"key":"5587_CR7","doi-asserted-by":"publisher","unstructured":"El Hajla, S., El Mahfoud, E., Yassine, M., Mounir, S.: Attack and anomaly detection in IoT Networks using machine learning approaches. p. 7. (2023). https:\/\/doi.org\/10.1109\/WINCOM59760.2023.10322991","DOI":"10.1109\/WINCOM59760.2023.10322991"},{"key":"5587_CR8","doi-asserted-by":"publisher","first-page":"100666","DOI":"10.1016\/j.cosrev.2024.100666","volume":"54","author":"Z Amiri","year":"2024","unstructured":"Amiri, Z., Heidari, A., Jafari, N., Hosseinzadeh, M.: Deep study on autonomous learning techniques for complex pattern recognition in interconnected information systems. Comput. Sci. Rev. 54, 100666 (2024). https:\/\/doi.org\/10.1016\/j.cosrev.2024.100666","journal-title":"Comput. Sci. Rev."},{"key":"5587_CR9","doi-asserted-by":"publisher","unstructured":"Drainakis, G., Katsaros, K., Pantazopoulos, P., Sourlas, V., Amditis, A.: Federated vs. Centralized Machine Learning under Privacy-elastic Users: A Comparative Analysis. (2020). https:\/\/doi.org\/10.1109\/NCA51143.2020.9306745","DOI":"10.1109\/NCA51143.2020.9306745"},{"key":"5587_CR10","doi-asserted-by":"publisher","unstructured":"Amiri, Z., Heidari, A., Navimipour, N.J.: \u2018Comprehensive survey of artificial intelligence techniques and strategies for climate change mitigation\u2019, Energy, 308, 132827 (2024). https:\/\/doi.org\/10.1016\/j.energy.2024.132827","DOI":"10.1016\/j.energy.2024.132827"},{"key":"5587_CR11","doi-asserted-by":"publisher","unstructured":"Md, M., Alam, et al.: Dec., \u2018Federated Ensemble-Learning for Transport Mode Detection in Vehicular Edge Network\u2019, Future Gener. Comput. Syst.\u00a0149,\u00a0 89\u2013104 (2023). https:\/\/doi.org\/10.1016\/j.future.2023.07.022","DOI":"10.1016\/j.future.2023.07.022"},{"key":"5587_CR12","doi-asserted-by":"publisher","DOI":"10.1007\/s10586-024-04711-0","author":"B Radjaa","year":"2024","unstructured":"Radjaa, B., Labraoui, N., Saidi, H., Bany Salameh, H.: Securing fog-assisted IoT smart homes: a federated learning-based intrusion detection approach. Clust Comput. (2024). https:\/\/doi.org\/10.1007\/s10586-024-04711-0","journal-title":"Clust Comput."},{"issue":"2","key":"5587_CR13","doi-asserted-by":"publisher","first-page":"513","DOI":"10.1007\/s13042-022-01647-y","volume":"14","author":"J Wen","year":"2023","unstructured":"Wen, J., Zhang, Z., Lan, Y., Cui, Z., Cai, J., Zhang, W.: A survey on federated learning: challenges and applications. Int. J. Mach. Learn. Cybern 14(2), 513\u2013535 (2023). https:\/\/doi.org\/10.1007\/s13042-022-01647-y","journal-title":"Int. J. Mach. Learn. Cybern"},{"key":"5587_CR14","unstructured":"Krishnan, S., Neyaz, A., Liu, Q.: \u2018IoT Network Attack Detection using Supervised Machine Learning\u2019, 10,\u00a018\u201332 (2021)"},{"key":"5587_CR15","doi-asserted-by":"publisher","unstructured":"Deshmukh, A., Ravulakollu, K.: \u2018An Efficient CNN-Based Intrusion Detection System for IoT: Use Case Towards Cybersecurity\u2019, Technologies, 12, 203 (2024). https:\/\/doi.org\/10.3390\/technologies12100203","DOI":"10.3390\/technologies12100203"},{"key":"5587_CR16","doi-asserted-by":"publisher","unstructured":"Xu, H., Sun, L., Fan, G., Li, W., Kuang, G. Kuang,.: \u2018A Hierarchical Intrusion Detection Model Combining Multiple Deep Learning Models With Attention Mechanism\u2019, IEEE Access. 1\u20131 (2023). https:\/\/doi.org\/10.1109\/ACCESS.2023.3290613","DOI":"10.1109\/ACCESS.2023.3290613"},{"key":"5587_CR17","doi-asserted-by":"publisher","unstructured":"Susilo, B., Sari, R.F.: Intrusion Detection in IoT Networks Using Deep Learning Algorithm\u2019, Information. Art no 5 11(5) (2020). https:\/\/doi.org\/10.3390\/info11050279","DOI":"10.3390\/info11050279"},{"key":"5587_CR18","doi-asserted-by":"publisher","unstructured":"Azumah, S., Elsayed, N., Adewopo, V., Zaghloul, Z., Li, C.: A deep LSTM based approach for intrusion detection IoT devices network in smart home. p. 841. (2021). https:\/\/doi.org\/10.1109\/WF-IoT51360.2021.9596033","DOI":"10.1109\/WF-IoT51360.2021.9596033"},{"key":"5587_CR19","doi-asserted-by":"publisher","unstructured":"Friha, O., Ferrag, M.A., Shu, L., Maglaras, L., Choo, K.-K.R., Nafaa, M.: \u2018FELIDS: Federated learning-based intrusion detection system for agricultural Internet of Things\u2019, J. Parallel Distrib. Comput.\u00a0165, 17\u201331 (2022). https:\/\/doi.org\/10.1016\/j.jpdc.2022.03.003","DOI":"10.1016\/j.jpdc.2022.03.003"},{"key":"5587_CR20","doi-asserted-by":"publisher","unstructured":"Mahfoud, E.E., El Hajla, S., Yassine, M., Mounir, S., 937\u2009~\u2009947, S.: doi: https:\/\/doi.org\/10.11591\/ijeecs.v37.i2.pp937-947. (2024)","DOI":"10.11591\/ijeecs.v37.i2.pp937-947"},{"key":"5587_CR21","doi-asserted-by":"publisher","unstructured":"Nguyen, T.D., Marchal, S., Miettinen, M., Fereidooni, H., Asokan, N., Sadeghi, A.-R.: \u2018D\u00cfoT: A Federated Self-learning Anomaly Detection System for IoT\u2019, in 2019 IEEE 39th International Conference on Distributed Computing Systems (ICDCS), Jul. pp. 756\u2013767. (2019)\u00a0https:\/\/doi.org\/10.1109\/ICDCS.2019.00080","DOI":"10.1109\/ICDCS.2019.00080"},{"key":"5587_CR22","doi-asserted-by":"publisher","unstructured":"Zhao, Y., Chen, J., Wu, D., Teng, J., Yu, S.: Multi-Task Network Anomaly Detection using Federated Learning. p. 279. (2019). https:\/\/doi.org\/10.1145\/3368926.3369705","DOI":"10.1145\/3368926.3369705"},{"key":"5587_CR23","doi-asserted-by":"publisher","unstructured":"Rahman, S.A., Tout, H., Talhi, C., Mourad, A.: \u2018Internet of Things Intrusion Detection: Centralized, On-Device, or Federated Learning?\u2019, IEEE Netw.\u00a034(6), 310\u2013317 (2020). https:\/\/doi.org\/10.1109\/MNET.011.2000286","DOI":"10.1109\/MNET.011.2000286"},{"key":"5587_CR24","doi-asserted-by":"publisher","unstructured":"Hazman, C., Guezzaz, A., Benkirane, S., Azrour, M.: Aug., \u2018Toward an intrusion detection model for IoT-based smart environments\u2019, multimed. Tools Appl., 83, (2023). https:\/\/doi.org\/10.1007\/s11042-023-16436-0","DOI":"10.1007\/s11042-023-16436-0"},{"key":"5587_CR25","doi-asserted-by":"publisher","unstructured":"El Hajla, S., El Mahfoud, E., Yassine, M., Mounir, S.: Enhancing Internet of Things Network Security Through an Ensemble-Learning Approach. p. 7. (2024). https:\/\/doi.org\/10.1145\/3659677.3659835","DOI":"10.1145\/3659677.3659835"},{"key":"5587_CR26","doi-asserted-by":"publisher","first-page":"110941","DOI":"10.1016\/j.knosys.2023.110941","volume":"279","author":"R Lazzarini","year":"2023","unstructured":"Lazzarini, R., Tianfield, H., Charissis, V.: A stacking ensemble of deep learning models for IoT intrusion detection. Knowl. -Based Syst. 279, 110941 (2023). https:\/\/doi.org\/10.1016\/j.knosys.2023.110941","journal-title":"Knowl. -Based Syst."},{"key":"5587_CR27","doi-asserted-by":"publisher","unstructured":"Bhati, N.S., Khari, M.: \u2018A new ensemble based approach for intrusion detection system using voting\u2019, J. Intell. Fuzzy Syst.\u00a042, 1\u201311 (2021). https:\/\/doi.org\/10.3233\/JIFS-189764","DOI":"10.3233\/JIFS-189764"},{"key":"5587_CR28","doi-asserted-by":"publisher","unstructured":"Khan, F., Jan, M.A., Alturki, R., Alshehri, M.D., Shah, S.T., ur Rehman, A.: \u2018A Secure Ensemble Learning-Based Fog-Cloud Approach for Cyberattack Detection in IoMT\u2019, IEEE Trans. Ind. Inform.\u00a019(10), 10125\u201310132 (2023). https:\/\/doi.org\/10.1109\/TII.2022.3231424","DOI":"10.1109\/TII.2022.3231424"},{"key":"5587_CR29","doi-asserted-by":"publisher","unstructured":"Mohamed, D., Ismael, O.: Enhancement of an IoT hybrid intrusion detection system based on fog-to-cloud computing. J. Cloud Comput. 12\u00a0(2023). https:\/\/doi.org\/10.1186\/s13677-023-00420-y","DOI":"10.1186\/s13677-023-00420-y"},{"key":"5587_CR30","doi-asserted-by":"publisher","unstructured":"El Houda, Z.A., Moudoud, H., Khoukhi, L.: \u2018Secure and Efficient Federated Learning for Robust Intrusion Detection in IoT Networks\u2019, in GLOBECOM 2023\u20132023 IEEE Global Communications Conference, Kuala Lumpur, Malaysia: IEEE, Dec. pp. 2668\u20132673. (2023). https:\/\/doi.org\/10.1109\/GLOBECOM54140.2023.10436768","DOI":"10.1109\/GLOBECOM54140.2023.10436768"},{"key":"5587_CR31","doi-asserted-by":"publisher","unstructured":"Houda, Z.A.E., Moudoud, H., Brik, B., Adil, M.: \u2018A Privacy-Preserving Framework for Efficient Network Intrusion Detection in Consumer Network Using Quantum Federated Learning\u2019, IEEE Trans. Consum. Electron. (2024). https:\/\/doi.org\/10.1109\/TCE.2024.3458985","DOI":"10.1109\/TCE.2024.3458985"},{"key":"5587_CR32","doi-asserted-by":"publisher","unstructured":"Shi, N., Lai, F., Kontar, R.A., Chowdhury, M.: \u2018Fed-ensemble: Improving Generalization through Model Ensembling in Federated Learning\u2019, IEEE Trans. Autom. Sci. Eng.\u00a021(3), 2792\u20132803 (2024). https:\/\/doi.org\/10.1109\/TASE.2023.3269639","DOI":"10.1109\/TASE.2023.3269639"},{"key":"5587_CR33","doi-asserted-by":"publisher","unstructured":"Gayathri, S., Surendran, D.: \u2018Unified ensemble federated learning with cloud computing for online anomaly detection in energy-efficient wireless sensor networks\u2019, J Cloud Comput, vol. 13, no. 1, f\u00e9vrier (2024). https:\/\/doi.org\/10.1186\/s13677-024-00595-y","DOI":"10.1186\/s13677-024-00595-y"},{"key":"5587_CR34","doi-asserted-by":"publisher","unstructured":"Attota, D.C., Mothukuri, V., Parizi, R.M., Pouriyeh, S.: \u2018An ensemble Multi-View federated learning intrusion detection for iot\u2019, IEEE Access, 9, 117734\u2013117745, (2021). https:\/\/doi.org\/10.1109\/ACCESS.2021.3107337","DOI":"10.1109\/ACCESS.2021.3107337"},{"key":"5587_CR35","doi-asserted-by":"publisher","unstructured":"Koroniotis, N., Moustafa, N., Sitnikova, E., Turnbull, B.: \u2018Towards the development of realistic botnet dataset in the Internet of Things for network forensic analytics: Bot-IoT dataset\u2019, Future Gener. Comput. Syst. 100, 779\u2013796 (2019). https:\/\/doi.org\/10.1016\/j.future.2019.05.041","DOI":"10.1016\/j.future.2019.05.041"},{"key":"5587_CR36","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2022.3165809","author":"MA Ferrag","year":"2022","unstructured":"Ferrag, M. A., Friha, O., Hamouda, D., Maglaras, L., Janicke, H.: \u2018Edge-IIoTset: A New Comprehensive Realistic Cyber Security Dataset of IoT and IIoT Applications for Centralized and Federated Learning\u2019. IEEE Access. 10, 40281\u201340306 (2022). https:\/\/doi.org\/10.1109\/ACCESS.2022.3165809"},{"key":"5587_CR37","doi-asserted-by":"publisher","unstructured":"Elreedy, D., Atiya, A., Kamalov, F.: A theoretical distribution analysis of synthetic minority oversampling technique (SMOTE) for imbalanced learning. Mach. Learn. 113\u00a0(2023). https:\/\/doi.org\/10.1007\/s10994-022-06296-4","DOI":"10.1007\/s10994-022-06296-4"},{"key":"5587_CR38","doi-asserted-by":"publisher","unstructured":"Wang, S., Dai, Y., Shen, J., Xuan, J.: \u2018Research on expansion and classification of imbalanced data based on SMOTE algorithm\u2019. Sci. Rep. 11 (2021). https:\/\/doi.org\/10.1038\/s41598-021-03430-5\u00a0","DOI":"10.1038\/s41598-021-03430-5"},{"key":"5587_CR39","doi-asserted-by":"publisher","unstructured":"Liu, Y., Ma, Z., Yang, Y., Liu, X., Ma, J., Ren, K.: \u2018RevFRF: Enabling Cross-Domain Random Forest Training With Revocable Federated Learning\u2019, IEEE Trans. Dependable Secure Comput.\u00a019(6), 3671\u20133685 (2022). https:\/\/doi.org\/10.1109\/TDSC.2021.3104842","DOI":"10.1109\/TDSC.2021.3104842"},{"key":"5587_CR40","doi-asserted-by":"publisher","unstructured":"Hou, J., Su, M., Fu, A., Yu, Y.: \u2018Verifiable Privacy-Preserving Scheme Based on Vertical Federated Random Forest\u2019, IEEE Internet Things J. 9(22) 22158\u201322172 (2022). https:\/\/doi.org\/10.1109\/JIOT.2021.3090951","DOI":"10.1109\/JIOT.2021.3090951"},{"key":"5587_CR41","doi-asserted-by":"publisher","unstructured":"Yao, H., Wang, J., Dai, P., Bo, L., Chen, Y.: \u2018An efficient and robust system for vertically federated random forest\u2019. (2022), arxiv: arxiv:2201.10761. https:\/\/doi.org\/10.48550\/arXiv.2201.10761","DOI":"10.48550\/arXiv.2201.10761"},{"key":"5587_CR42","doi-asserted-by":"publisher","unstructured":"Wang, Z., Gai, K.: \u2018Decision Tree-Based Federated Learning: A Survey\u2019, Blockchains.\u00a02(1), 1 (2024). https:\/\/doi.org\/10.3390\/blockchains2010003","DOI":"10.3390\/blockchains2010003"},{"key":"5587_CR43","doi-asserted-by":"publisher","unstructured":"Wu, Y., Cai, S., Xiao, X., Chen, G., Ooi, B.C.: \u2018Privacy Preserving Vertical Federated Learning for Tree-based Models\u2019, Proc. VLDB Endow.\u00a013(12), 2090\u20132103 (2020). https:\/\/doi.org\/10.14778\/3407790.3407811","DOI":"10.14778\/3407790.3407811"},{"key":"5587_CR44","doi-asserted-by":"publisher","first-page":"43954","DOI":"10.1109\/ACCESS.2022.3169502","volume":"10","author":"F Yamamoto","year":"2022","unstructured":"Yamamoto, F., Ozawa, S., Wang, L.: eFL-Boost: Efficient federated learning for gradient boosting decision trees. IEEE Access. 10, 43954\u201343963 (2022). https:\/\/doi.org\/10.1109\/ACCESS.2022.3169502","journal-title":"IEEE Access."},{"key":"5587_CR45","doi-asserted-by":"publisher","first-page":"1022","DOI":"10.1109\/TIFS.2022.3232955","volume":"18","author":"J Zhao","year":"2023","unstructured":"Zhao, J., Zhu, H., Xu, W., Wang, F., Lu, R., Li, H.: SGBoost: An efficient and Privacy-Preserving vertical federated tree boosting framework. IEEE Trans. Inf. Forensics Secur. 18, 1022\u20131036 (2023). https:\/\/doi.org\/10.1109\/TIFS.2022.3232955","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"key":"5587_CR46","doi-asserted-by":"publisher","unstructured":"Wang, R., Ersoy, O., Zhu, H., Jin, Y., Liang, K.: \u2018FEVERLESS: Fast and Secure Vertical Federated Learning based on XGBoost for Decentralized Labels\u2019, IEEE Trans. Big Data. 1\u201315 (2022). https:\/\/doi.org\/10.1109\/TBDATA.2022.3227326","DOI":"10.1109\/TBDATA.2022.3227326"},{"key":"5587_CR47","doi-asserted-by":"publisher","unstructured":"Rashid, M.M., Khan, S., Eusufzai, F., Redwan, A., Sabuj, S., Elsharief, M.: \u2018A Federated Learning-Based Approach for Improving Intrusion Detection in Industrial Internet of Things Networks\u2019, Network. 3,\u00a0158\u2013179 (2023). https:\/\/doi.org\/10.3390\/network3010008","DOI":"10.3390\/network3010008"},{"key":"5587_CR48","doi-asserted-by":"publisher","first-page":"52215","DOI":"10.1109\/ACCESS.2024.3386631","volume":"12","author":"MH Bhavsar","year":"2024","unstructured":"Bhavsar, M.H., Bekele, Y.B., Roy, K., Kelly, J.C., Limbrick, D.: FL-IDS: Federated Learning-Based intrusion detection system using edge devices for transportation IoT. IEEE Access. 12, 52215\u201352226 (2024). https:\/\/doi.org\/10.1109\/ACCESS.2024.3386631","journal-title":"IEEE Access."},{"key":"5587_CR49","doi-asserted-by":"publisher","unstructured":"Mohammed, M.Q., Alrahman, Z.A., Shehab, A.R.: Investigating Intrusion Detection System Using Federated Learning for IoT Security Challenges. Iraqi J. Comput. Sci. Math. 5(4), (2024). https:\/\/doi.org\/10.52866\/2788-7421.1218","DOI":"10.52866\/2788-7421.1218"},{"key":"5587_CR50","doi-asserted-by":"publisher","unstructured":"Karimy, A.U., Reddy, P.C.: \u2018Enhancing IoT Security: A Novel Approach with Federated Learning and Differential Privacy Integration\u2019. Int. J. Comput. Netw. Commun.\u00a016(4), 01\u201317 (2024). https:\/\/doi.org\/10.5121\/ijcnc.2024.16401","DOI":"10.5121\/ijcnc.2024.16401"},{"key":"5587_CR51","doi-asserted-by":"publisher","unstructured":"Begum, K., Mozumder, M.A., Joo, M.-I., Kim, H.-C.: \u2018BFLIDS: Blockchain-Driven Federated Learning for Intrusion Detection in IoMT Networks\u2019, Sensors, 24,\u00a0(2024). https:\/\/doi.org\/10.3390\/s24144591","DOI":"10.3390\/s24144591"},{"key":"5587_CR52","doi-asserted-by":"publisher","unstructured":"Olanrewaju-George, B., Pranggono, B.: Federated learning-based intrusion detection system for the internet of things using unsupervised and supervised deep learning models. Cyber Secur. Appl. 3, 100068 (2025). https:\/\/doi.org\/10.1016\/j.csa.2024.100068","DOI":"10.1016\/j.csa.2024.100068"}],"container-title":["Cluster Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10586-025-05587-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10586-025-05587-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10586-025-05587-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,3]],"date-time":"2025-11-03T17:54:21Z","timestamp":1762192461000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10586-025-05587-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9,19]]},"references-count":52,"journal-issue":{"issue":"13","published-print":{"date-parts":[[2025,11]]}},"alternative-id":["5587"],"URL":"https:\/\/doi.org\/10.1007\/s10586-025-05587-4","relation":{"references":[{"id-type":"doi","id":"10.1109\/ACCESS.2022.3165809","asserted-by":"subject"}]},"ISSN":["1386-7857","1573-7543"],"issn-type":[{"type":"print","value":"1386-7857"},{"type":"electronic","value":"1573-7543"}],"subject":[],"published":{"date-parts":[[2025,9,19]]},"assertion":[{"value":"20 February 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"5 June 2025","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 June 2025","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"19 September 2025","order":4,"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 competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}},{"value":"The authors declare that they have no relevant financial or non-financial interests to disclose.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Financial statement"}}],"article-number":"819"}}