{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,7]],"date-time":"2026-03-07T02:22:32Z","timestamp":1772850152324,"version":"3.50.1"},"reference-count":50,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2025,2,25]],"date-time":"2025-02-25T00:00:00Z","timestamp":1740441600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,2,25]],"date-time":"2025-02-25T00:00:00Z","timestamp":1740441600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["62172441"],"award-info":[{"award-number":["62172441"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of Chin","doi-asserted-by":"crossref","award":["61772553"],"award-info":[{"award-number":["61772553"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Local Science and Technology Developing Foundation Guided by Central Government","award":["2021Szvup166"],"award-info":[{"award-number":["2021Szvup166"]}]},{"name":"Opening Project of State Key Laboratory of Nickel and Cobalt Resources Comprehensive Utilization","award":["GZSYS-KY-2022-018"],"award-info":[{"award-number":["GZSYS-KY-2022-018"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Cluster Comput"],"published-print":{"date-parts":[[2025,8]]},"DOI":"10.1007\/s10586-024-04943-0","type":"journal-article","created":{"date-parts":[[2025,2,25]],"date-time":"2025-02-25T14:05:54Z","timestamp":1740492354000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":23,"title":["Securing internet of vehicles: a blockchain-based federated learning approach for enhanced intrusion detection"],"prefix":"10.1007","volume":"28","author":[{"given":"Irshad","family":"Ullah","sequence":"first","affiliation":[]},{"given":"Xiaoheng","family":"Deng","sequence":"additional","affiliation":[]},{"given":"Xinjun","family":"Pei","sequence":"additional","affiliation":[]},{"given":"Husnain","family":"Mushtaq","sequence":"additional","affiliation":[]},{"given":"Zia","family":"Khan","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,2,25]]},"reference":[{"issue":"10","key":"4943_CR1","doi-asserted-by":"publisher","first-page":"6663","DOI":"10.1109\/TII.2019.2962844","volume":"16","author":"T Wang","year":"2019","unstructured":"Wang, T., Cao, Z., Wang, S., Wang, J., Qi, L., Liu, A., Xie, M., Li, X.: Privacy-enhanced data collection based on deep learning for internet of vehicles. IEEE Trans. Indust. Inform. 16(10), 6663\u20136672 (2019)","journal-title":"IEEE Trans. Indust. Inform."},{"issue":"6","key":"4943_CR2","doi-asserted-by":"publisher","first-page":"6798","DOI":"10.1109\/TVT.2020.2984369","volume":"69","author":"SR Pokhrel","year":"2020","unstructured":"Pokhrel, S.R., Choi, J.: Improving tcp performance over wifi for internet of vehicles: a federated learning approach. IEEE Trans. Veh. Technol 69(6), 6798\u20136802 (2020)","journal-title":"IEEE Trans. Veh. Technol"},{"issue":"6","key":"4943_CR3","doi-asserted-by":"publisher","first-page":"67","DOI":"10.1109\/MCE.2021.3097705","volume":"11","author":"B Ghimire","year":"2021","unstructured":"Ghimire, B., Rawat, D.B.: Secure, privacy preserving, and verifiable federating learning using blockchain for internet of vehicles. IEEE Consum. Electron. Mag. 11(6), 67\u201374 (2021)","journal-title":"IEEE Consum. Electron. Mag."},{"key":"4943_CR4","doi-asserted-by":"crossref","unstructured":"Scott, C., Khan, M.S., Paranjothi, A., Li, J.Q.: Enabling rural iov communication through decentralized clustering and federated learning. In: 2024 IEEE 14th Annual Computing and Communication Workshop and Conference (CCWC), pp. 0228\u20130234 (2024). IEEE","DOI":"10.1109\/CCWC60891.2024.10427882"},{"key":"4943_CR5","doi-asserted-by":"crossref","unstructured":"Ullah, I., Deng, X., Pei, X., Mushtaq, H., Uzair, M.: Iov-sfl: A blockchain-based federated learning framework for secure and efficient data sharing in the internet of vehicles (2023)","DOI":"10.21203\/rs.3.rs-3648280\/v1"},{"issue":"2","key":"4943_CR6","doi-asserted-by":"publisher","first-page":"2455","DOI":"10.1109\/JSYST.2023.3236995","volume":"17","author":"J Li","year":"2023","unstructured":"Li, J., Tong, X., Liu, J., Cheng, L.: An efficient federated learning system for network intrusion detection. IEEE Syst. J. 17(2), 2455\u20132464 (2023)","journal-title":"IEEE Syst. J."},{"issue":"9","key":"4943_CR7","doi-asserted-by":"publisher","first-page":"421","DOI":"10.1177\/03611981231159118","volume":"2677","author":"Q Xu","year":"2023","unstructured":"Xu, Q., Zhang, L., Ou, D., Yu, W.: Secure intrusion detection by differentially private federated learning for inter-vehicle networks. Trans. Res. Rec. 2677(9), 421\u2013437 (2023)","journal-title":"Trans. Res. Rec."},{"key":"4943_CR8","doi-asserted-by":"publisher","first-page":"103320","DOI":"10.1016\/j.adhoc.2023.103320","volume":"152","author":"S Ali","year":"2024","unstructured":"Ali, S., Li, Q., Yousafzai, A.: Blockchain and federated learning-based intrusion detection approaches for edge-enabled industrial iot networks: a survey. Ad Hoc Netw. 152, 103320 (2024)","journal-title":"Ad Hoc Netw."},{"key":"4943_CR9","doi-asserted-by":"crossref","unstructured":"Abou El\u00a0Houda, Z., Moudoud, H., Brik, B., Khoukhi, L.: Blockchain-enabled federated learning for enhanced collaborative intrusion detection in vehicular edge computing. IEEE Transactions on Intelligent Transportation Systems (2024)","DOI":"10.1109\/TITS.2024.3351699"},{"issue":"4","key":"4943_CR10","doi-asserted-by":"publisher","first-page":"2716","DOI":"10.1109\/TII.2019.2956474","volume":"16","author":"F Farivar","year":"2019","unstructured":"Farivar, F., Haghighi, M.S., Jolfaei, A., Alazab, M.: Artificial intelligence for detection, estimation, and compensation of malicious attacks in nonlinear cyber-physical systems and industrial iot. IEEE Trans. Indust. Inform. 16(4), 2716\u20132725 (2019)","journal-title":"IEEE Trans. Indust. Inform."},{"issue":"5","key":"4943_CR11","doi-asserted-by":"publisher","first-page":"2256","DOI":"10.1007\/s12083-023-01531-8","volume":"16","author":"I Ullah","year":"2023","unstructured":"Ullah, I., Deng, X., Pei, X., Jiang, P., Mushtaq, H.: A verifiable and privacy-preserving blockchain-based federated learning approach. Peer-to-Peer Netw. Appl. 16(5), 2256\u20132270 (2023)","journal-title":"Peer-to-Peer Netw. Appl."},{"issue":"16","key":"4943_CR12","first-page":"12518","volume":"8","author":"G D\u2019Angelo","year":"2020","unstructured":"D\u2019Angelo, G., Castiglione, A., Palmieri, F.: A cluster-based multidimensional approach for detecting attacks on connected vehicles. IEEE Int. Things J. 8(16), 12518\u201312527 (2020)","journal-title":"IEEE Int. Things J."},{"key":"4943_CR13","unstructured":"Janzing, D., Minorics, L., Bl\u00f6baum, P.: Feature relevance quantification in explainable ai: A causal problem. In: International Conference on Artificial Intelligence and Statistics, pp. 2907\u20132916 (2020). PMLR"},{"issue":"7","key":"4943_CR14","doi-asserted-by":"publisher","first-page":"4904","DOI":"10.1109\/TII.2020.2968923","volume":"17","author":"J Feng","year":"2020","unstructured":"Feng, J., Yang, L.T., Zhang, R., Gavuna, B.S.: Privacy-preserving tucker train decomposition over blockchain-based encrypted industrial iot data. IEEE Trans. Indust. Inform. 17(7), 4904\u20134913 (2020)","journal-title":"IEEE Trans. Indust. Inform."},{"issue":"2","key":"4943_CR15","doi-asserted-by":"publisher","first-page":"882","DOI":"10.1109\/TII.2019.2962759","volume":"17","author":"Y Chen","year":"2019","unstructured":"Chen, Y., Hu, W., Alam, M., Wu, T.: Fiden: intelligent fingerprint learning for attacker identification in the industrial internet of things. IEEE Trans. Indust. Inform. 17(2), 882\u2013890 (2019)","journal-title":"IEEE Trans. Indust. Inform."},{"key":"4943_CR16","unstructured":"Singh, M., Kim, S.: Blockchain based intelligent vehicle data sharing framework. arXiv preprint arXiv:1708.09721 (2017)"},{"issue":"8","key":"4943_CR17","doi-asserted-by":"publisher","first-page":"4889","DOI":"10.1109\/TITS.2020.2983466","volume":"22","author":"Q Kong","year":"2020","unstructured":"Kong, Q., Su, L., Ma, M.: Achieving privacy-preserving and verifiable data sharing in vehicular fog with blockchain. IEEE Trans. Intell. Trans. Syst. 22(8), 4889\u20134898 (2020)","journal-title":"IEEE Trans. Intell. Trans. Syst."},{"issue":"5","key":"4943_CR18","doi-asserted-by":"publisher","first-page":"3469","DOI":"10.1109\/TII.2020.3022432","volume":"17","author":"X Zhou","year":"2020","unstructured":"Zhou, X., Hu, Y., Liang, W., Ma, J., Jin, Q.: Variational lstm enhanced anomaly detection for industrial big data. IEEE Trans. Indust. Inform. 17(5), 3469\u20133477 (2020)","journal-title":"IEEE Trans. Indust. Inform."},{"issue":"8","key":"4943_CR19","doi-asserted-by":"publisher","first-page":"5615","DOI":"10.1109\/TII.2020.3023430","volume":"17","author":"B Li","year":"2020","unstructured":"Li, B., Wu, Y., Song, J., Lu, R., Li, T., Zhao, L.: Deepfed: federated deep learning for intrusion detection in industrial cyber-physical systems. IEEE Trans. Indust. Inform. 17(8), 5615\u20135624 (2020)","journal-title":"IEEE Trans. Indust. Inform."},{"issue":"1","key":"4943_CR20","first-page":"144","volume":"8","author":"Y Cheng","year":"2020","unstructured":"Cheng, Y., Xu, Y., Zhong, H., Liu, Y.: Leveraging semisupervised hierarchical stacking temporal convolutional network for anomaly detection in iot communication. IEEE Int. Things J. 8(1), 144\u2013155 (2020)","journal-title":"IEEE Int. Things J."},{"issue":"2","key":"4943_CR21","first-page":"951","volume":"8","author":"J Gao","year":"2020","unstructured":"Gao, J., Gan, L., Buschendorf, F., Zhang, L., Liu, H., Li, P., Dong, X., Lu, T.: Omni scada intrusion detection using deep learning algorithms. IEEE Int. Things J. 8(2), 951\u2013961 (2020)","journal-title":"IEEE Int. Things J."},{"issue":"4","key":"4943_CR22","first-page":"2545","volume":"9","author":"V Mothukuri","year":"2021","unstructured":"Mothukuri, V., Khare, P., Parizi, R.M., Pouriyeh, S., Dehghantanha, A., Srivastava, G.: Federated-learning-based anomaly detection for iot security attacks. IEEE Int. Things J. 9(4), 2545\u20132554 (2021)","journal-title":"IEEE Int. Things J."},{"issue":"5","key":"4943_CR23","doi-asserted-by":"publisher","first-page":"3492","DOI":"10.1109\/TII.2021.3107783","volume":"18","author":"L Cui","year":"2021","unstructured":"Cui, L., Qu, Y., Xie, G., Zeng, D., Li, R., Shen, S., Yu, S.: Security and privacy-enhanced federated learning for anomaly detection in iot infrastructures. IEEE Trans. Indust. Inform. 18(5), 3492\u20133500 (2021)","journal-title":"IEEE Trans. Indust. Inform."},{"issue":"8","key":"4943_CR24","doi-asserted-by":"publisher","first-page":"6348","DOI":"10.1109\/JIOT.2020.3011726","volume":"8","author":"Y Liu","year":"2020","unstructured":"Liu, Y., Garg, S., Nie, J., Zhang, Y., Xiong, Z., Kang, J., Hossain, M.S.: Deep anomaly detection for time-series data in industrial iot: a communication-efficient on-device federated learning approach. IEEE Inter. Things J. 8(8), 6348\u20136358 (2020)","journal-title":"IEEE Inter. Things J."},{"issue":"1","key":"4943_CR25","first-page":"29","volume":"17","author":"SB Mallampati","year":"2023","unstructured":"Mallampati, S.B., Hari, S.: A review on recent approaches of machine learning, deep learning, and explainable artificial intelligence in intrusion detection systems. Majl. J. Electr. Eng. 17(1), 29\u201354 (2023)","journal-title":"Majl. J. Electr. Eng."},{"key":"4943_CR26","doi-asserted-by":"crossref","unstructured":"Wu, Q., Wang, S., Fan, P., Fan, Q.: Deep reinforcement learning based vehicle selection for asynchronous federated learning enabled vehicular edge computing. In: International Congress on Communications, Networking, and Information Systems, pp. 3\u201326 (2023). Springer","DOI":"10.1007\/978-981-99-3581-9_1"},{"issue":"2","key":"4943_CR27","doi-asserted-by":"publisher","first-page":"26","DOI":"10.1109\/IOTM.001.2200233","volume":"6","author":"A Boualouache","year":"2023","unstructured":"Boualouache, A., Brik, B., Senouci, S.-M., Engel, T.: On-demand security framework for 5gb vehicular networks. IEEE Int. Things Mag. 6(2), 26\u201331 (2023)","journal-title":"IEEE Int. Things Mag."},{"issue":"6","key":"4943_CR28","doi-asserted-by":"publisher","first-page":"6073","DOI":"10.1109\/TVT.2021.3076780","volume":"70","author":"H Liu","year":"2021","unstructured":"Liu, H., Zhang, S., Zhang, P., Zhou, X., Shao, X., Pu, G., Zhang, Y.: Blockchain and federated learning for collaborative intrusion detection in vehicular edge computing. IEEE Trans. Veh. Technol. 70(6), 6073\u20136084 (2021)","journal-title":"IEEE Trans. Veh. Technol."},{"issue":"9","key":"4943_CR29","doi-asserted-by":"publisher","first-page":"16492","DOI":"10.1109\/TITS.2021.3098636","volume":"23","author":"R Kumar","year":"2021","unstructured":"Kumar, R., Kumar, P., Tripathi, R., Gupta, G.P., Kumar, N., Hassan, M.M.: A privacy-preserving-based secure framework using blockchain-enabled deep-learning in cooperative intelligent transport system. IEEE Trans. Intell. Trans. Syst. 23(9), 16492\u201316503 (2021)","journal-title":"IEEE Trans. Intell. Trans. Syst."},{"issue":"3","key":"4943_CR30","first-page":"1817","volume":"8","author":"Y Zhao","year":"2020","unstructured":"Zhao, Y., Zhao, J., Jiang, L., Tan, R., Niyato, D., Li, Z., Lyu, L., Liu, Y.: Privacy-preserving blockchain-based federated learning for iot devices. IEEE Int. Things J. 8(3), 1817\u20131829 (2020)","journal-title":"IEEE Int. Things J."},{"issue":"4","key":"4943_CR31","first-page":"2300","volume":"8","author":"Y Wu","year":"2020","unstructured":"Wu, Y., Dai, H.-N., Wang, H.: Convergence of blockchain and edge computing for secure and scalable iiot critical infrastructures in industry 40. IEEE Int. Things J. 8(4), 2300\u20132317 (2020)","journal-title":"IEEE Int. Things J."},{"issue":"4","key":"4943_CR32","doi-asserted-by":"publisher","first-page":"4298","DOI":"10.1109\/TVT.2020.2973651","volume":"69","author":"Y Lu","year":"2020","unstructured":"Lu, Y., Huang, X., Zhang, K., Maharjan, S., Zhang, Y.: Blockchain empowered asynchronous federated learning for secure data sharing in internet of vehicles. IEEE Trans. Veh. Technol. 69(4), 4298\u20134311 (2020)","journal-title":"IEEE Trans. Veh. Technol."},{"key":"4943_CR33","first-page":"1","volume":"2021","author":"Y Peng","year":"2021","unstructured":"Peng, Y., Chen, Z., Chen, Z., Ou, W., Han, W., Ma, J.: Bflp: an adaptive federated learning framework for internet of vehicles. Mob. Inform. Syst. 2021, 1\u201318 (2021)","journal-title":"Mob. Inform. Syst."},{"issue":"11","key":"4943_CR34","first-page":"8836","volume":"8","author":"Y Zhao","year":"2020","unstructured":"Zhao, Y., Zhao, J., Yang, M., Wang, T., Wang, N., Lyu, L., Niyato, D., Lam, K.-Y.: Local differential privacy-based federated learning for internet of things. IEEE Int. Things J. 8(11), 8836\u20138853 (2020)","journal-title":"IEEE Int. Things J."},{"issue":"7","key":"4943_CR35","doi-asserted-by":"publisher","first-page":"7385","DOI":"10.1109\/JSEN.2022.3153338","volume":"22","author":"P Zhao","year":"2022","unstructured":"Zhao, P., Huang, Y., Gao, J., Xing, L., Wu, H., Ma, H.: Federated learning-based collaborative authentication protocol for shared data in social iov. IEEE Sens. J. 22(7), 7385\u20137398 (2022)","journal-title":"IEEE Sens. J."},{"issue":"8","key":"4943_CR36","doi-asserted-by":"publisher","first-page":"7550","DOI":"10.1109\/TVT.2018.2828651","volume":"67","author":"G Sun","year":"2018","unstructured":"Sun, G., Zhang, Y., Liao, D., Yu, H., Du, X., Guizani, M.: Bus-trajectory-based street-centric routing for message delivery in urban vehicular ad hoc networks. IEEE Trans. Veh. Technol. 67(8), 7550\u20137563 (2018)","journal-title":"IEEE Trans. Veh. Technol."},{"key":"4943_CR37","doi-asserted-by":"crossref","unstructured":"Batista, R.C. Gustavo Enrique de Almeida\u00a0Prati, Monard, M.C.: A study of the behavior of several methods for balancing machine learning training data. ACM SIGKDD explorations newsletter 6(1), 20\u201329 (2004)","DOI":"10.1145\/1007730.1007735"},{"issue":"1","key":"4943_CR38","first-page":"4229924","volume":"2023","author":"M Lee","year":"2023","unstructured":"Lee, M.: Mathematical analysis and performance evaluation of the gelu activation function in deep learning. J. Math. 2023(1), 4229924 (2023)","journal-title":"J. Math."},{"issue":"8","key":"4943_CR39","first-page":"6348","volume":"8","author":"Y Liu","year":"2020","unstructured":"Liu, Y., Garg, S., Nie, J., Zhang, Y., Xiong, Z., Kang, J., Hossain, M.S.: Deep anomaly detection for time-series data in industrial iot: a communication-efficient on-device federated learning approach. IEEE Int. Things J. 8(8), 6348\u20136358 (2020)","journal-title":"IEEE Int. Things J."},{"issue":"4","key":"4943_CR40","first-page":"2545","volume":"9","author":"V Mothukuri","year":"2021","unstructured":"Mothukuri, V., Khare, P., Parizi, R.M., Pouriyeh, S., Dehghantanha, A., Srivastava, G.: Federated-learning-based anomaly detection for iot security attacks. IEEE Int. Things J. 9(4), 2545\u20132554 (2021)","journal-title":"IEEE Int. Things J."},{"issue":"8","key":"4943_CR41","doi-asserted-by":"publisher","first-page":"5615","DOI":"10.1109\/TII.2020.3023430","volume":"17","author":"B Li","year":"2020","unstructured":"Li, B., Wu, Y., Song, J., Lu, R., Li, T., Zhao, L.: Deepfed: federated deep learning for intrusion detection in industrial cyber-physical systems. IEEE Trans. Indust. Inform. 17(8), 5615\u20135624 (2020)","journal-title":"IEEE Trans. Indust. Inform."},{"issue":"3","key":"4943_CR42","doi-asserted-by":"publisher","first-page":"2523","DOI":"10.1109\/TITS.2021.3119968","volume":"23","author":"M Abdel-Basset","year":"2021","unstructured":"Abdel-Basset, M., Moustafa, N., Hawash, H., Razzak, I., Sallam, K.M., Elkomy, O.M.: Federated intrusion detection in blockchain-based smart transportation systems. IEEE Trans. Intell. Trans. Syst. 23(3), 2523\u20132537 (2021)","journal-title":"IEEE Trans. Intell. Trans. Syst."},{"issue":"5","key":"4943_CR43","first-page":"3930","volume":"9","author":"SI Popoola","year":"2021","unstructured":"Popoola, S.I., Ande, R., Adebisi, B., Gui, G., Hammoudeh, M., Jogunola, O.: Federated deep learning for zero-day botnet attack detection in iot-edge devices. IEEE Int. Things J. 9(5), 3930\u20133944 (2021)","journal-title":"IEEE Int. Things J."},{"key":"4943_CR44","doi-asserted-by":"crossref","unstructured":"Wu, P., Moustafa, N., Yang, S., Guo, H.: Densely connected residual network for attack recognition. In: 2020 IEEE 19th International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom), pp. 233\u2013242 (2020). IEEE","DOI":"10.1109\/TrustCom50675.2020.00042"},{"key":"4943_CR45","doi-asserted-by":"publisher","first-page":"142206","DOI":"10.1109\/ACCESS.2021.3120626","volume":"9","author":"AR Gad","year":"2021","unstructured":"Gad, A.R., Nashat, A.A., Barkat, T.M.: Intrusion detection system using machine learning for vehicular ad hoc networks based on ton-iot dataset. IEEE Access 9, 142206\u2013142217 (2021)","journal-title":"IEEE Access"},{"key":"4943_CR46","doi-asserted-by":"crossref","unstructured":"Abou\u00a0Khamis, R., Matrawy, A.: Evaluation of adversarial training on different types of neural networks in deep learning-based idss. In: 2020 International Symposium on Networks, Computers and Communications (ISNCC), pp. 1\u20136 (2020). IEEE","DOI":"10.1109\/ISNCC49221.2020.9297344"},{"key":"4943_CR47","doi-asserted-by":"crossref","unstructured":"Singh, P., Pankaj, A., Mitra, R., et al.: Edge-detect: Edge-centric network intrusion detection using deep neural network. In: 2021 IEEE 18th Annual Consumer Communications & Networking Conference (CCNC), pp. 1\u20136 (2021). IEEE","DOI":"10.1109\/CCNC49032.2021.9369469"},{"issue":"5","key":"4943_CR48","first-page":"433","volume":"7","author":"V Kanimozhi","year":"2019","unstructured":"Kanimozhi, V., Jacob, P.: Unsw-nb15 dataset feature selection and network intrusion detection using deep learning. Int. J. Recent Technol. Eng. 7(5), 433 (2019)","journal-title":"Int. J. Recent Technol. Eng."},{"key":"4943_CR49","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s40537-020-00379-6","volume":"7","author":"SM Kasongo","year":"2020","unstructured":"Kasongo, S.M., Sun, Y.: Performance analysis of intrusion detection systems using a feature selection method on the unsw-nb15 dataset. J. Big Data 7, 1\u201320 (2020)","journal-title":"J. Big Data"},{"issue":"19","key":"4943_CR50","doi-asserted-by":"publisher","first-page":"9572","DOI":"10.3390\/app12199572","volume":"12","author":"I Tareq","year":"2022","unstructured":"Tareq, I., Elbagoury, B.M., El-Regaily, S., El-Horbaty, E.-S.M.: Analysis of ton-iot, unw-nb15, and edge-iiot datasets using dl in cybersecurity for iot. Appl. Sci. 12(19), 9572 (2022)","journal-title":"Appl. Sci."}],"container-title":["Cluster Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10586-024-04943-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10586-024-04943-0\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10586-024-04943-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,6]],"date-time":"2025-09-06T06:32:26Z","timestamp":1757140346000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10586-024-04943-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,2,25]]},"references-count":50,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2025,8]]}},"alternative-id":["4943"],"URL":"https:\/\/doi.org\/10.1007\/s10586-024-04943-0","relation":{},"ISSN":["1386-7857","1573-7543"],"issn-type":[{"value":"1386-7857","type":"print"},{"value":"1573-7543","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,2,25]]},"assertion":[{"value":"1 May 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 September 2024","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 November 2024","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"25 February 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 affirm that there are no conflicting interests to declare. All co-authors have thoroughly reviewed and approved the manuscript\u2019s content, and there are no Conflict of interest to report. We certify that this submission represents original work and is not concurrently under review by any other publication.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"Not applicable","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics Approval"}},{"value":"All authors unanimously agree to publish the paper.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent to publish"}}],"article-number":"256"}}