{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,31]],"date-time":"2025-10-31T15:38:51Z","timestamp":1761925131624,"version":"build-2065373602"},"reference-count":20,"publisher":"Springer Science and Business Media LLC","issue":"12","license":[{"start":{"date-parts":[[2025,9,15]],"date-time":"2025-09-15T00:00:00Z","timestamp":1757894400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,9,15]],"date-time":"2025-09-15T00:00:00Z","timestamp":1757894400000},"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\/s10586-025-05496-6","type":"journal-article","created":{"date-parts":[[2025,9,15]],"date-time":"2025-09-15T19:22:40Z","timestamp":1757964160000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["MAEL: meta-active semi-supervised ensemble learning model for DDoS attack detection"],"prefix":"10.1007","volume":"28","author":[{"given":"Ahmed","family":"Saidane","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ali","family":"El Kamel","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Habib","family":"Youssef","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,9,15]]},"reference":[{"key":"5496_CR1","doi-asserted-by":"publisher","unstructured":"Alqahtani, H., Kumar, G.: A deep learning-based intrusion detection system for in-vehicle networks. Comput. Electr. Eng. 104, 108447 (2022). https:\/\/doi.org\/10.1016\/j.compeleceng.2022.108447, https:\/\/www.sciencedirect.com\/science\/article\/pii\/S0045790622006620","DOI":"10.1016\/j.compeleceng.2022.108447"},{"key":"5496_CR2","doi-asserted-by":"crossref","unstructured":"Barsellotti, L., Marinis, L.D., Cugini, F., et\u00a0al.: Ftg-net: Hierarchical flow-to-traffic graph neural network for ddos attack detection. IEEE 24th International Conference on High Performance Switching and Routing (HPSR) (2023)","DOI":"10.1109\/HPSR57248.2023.10147929"},{"key":"5496_CR3","unstructured":"Beck, J., Vuorio, R., Liu, E.Z., et al.: A survey of meta-reinforcement learning. Machine learning, Arxiv (2024)"},{"key":"5496_CR4","doi-asserted-by":"publisher","unstructured":"El Kamel, A., Eltaief, H., Youssef, H.: On-the-fly (d)dos attack mitigation in sdn using deep neural network-based rate limiting. Comput. Commun. 182, 153\u2013169 (2022). https:\/\/doi.org\/10.1016\/j.comcom.2021.11.003, https:\/\/www.sciencedirect.com\/science\/article\/pii\/S0140366421004308","DOI":"10.1016\/j.comcom.2021.11.003"},{"key":"5496_CR5","unstructured":"Finn, C., Abbeel, P., et\u00a0al.: Model-agnostic meta-learning for fast adaptation of deep networks. Proceedings of the 34th International Conference on Machine Learning (2017)"},{"key":"5496_CR6","doi-asserted-by":"crossref","unstructured":"Flesca, S., Mandaglio, D., Scala, F., et\u00a0al.: A meta-active learning approach exploiting instance importance. Exp. Syst. Appl. (2024)","DOI":"10.1016\/j.eswa.2024.123320"},{"key":"5496_CR7","doi-asserted-by":"crossref","unstructured":"Guo, W., Qiu, H., Liu, Z., et\u00a0al.: Gld-net: Deep learning to detect ddos attack via topological and traffic feature fusion. Comput. Intell. Neurosci. (2022)","DOI":"10.1155\/2022\/4611331"},{"key":"5496_CR8","doi-asserted-by":"publisher","unstructured":"El Kamel, A.: A gnn-based rate limiting framework for ddos attack mitigation in multi-controller sdn. IEEE Symposium on Computers and Communications (ISCC) (2023). https:\/\/doi.org\/10.1109\/ISCC58397.2023.10218204","DOI":"10.1109\/ISCC58397.2023.10218204"},{"key":"5496_CR9","doi-asserted-by":"publisher","unstructured":"Khan, I.A., Keshk, M., Pi, D., et al.: Enhancing iiot networks protection: A robust security model for attack detection in internet industrial control systems. Ad Hoc Netw. 134, 102930 (2022). https:\/\/doi.org\/10.1016\/j.adhoc.2022.102930, https:\/\/www.sciencedirect.com\/science\/article\/pii\/S1570870522001159","DOI":"10.1016\/j.adhoc.2022.102930"},{"key":"5496_CR10","unstructured":"Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional network. ArXiv:1609.02907 (2017)"},{"key":"5496_CR11","doi-asserted-by":"crossref","unstructured":"Li, Y., Li, R., Zhou, Z., et\u00a0al.: Graphddos: Effective ddos attack detection using graph neural networks. IEEE 25th International Conference on Computer Supported Cooperative Work in Design (CSCWD) (2022)","DOI":"10.1109\/CSCWD54268.2022.9776097"},{"key":"5496_CR12","doi-asserted-by":"publisher","unstructured":"Lo, W., Alqahtani, H., Thakur, K., et al.: A hybrid deep learning based intrusion detection system using spatial-temporal representation of in-vehicle network traffic. Veh. Commun. 35, 100471 (2022). https:\/\/doi.org\/10.1016\/j.vehcom.2022.100471, https:\/\/www.sciencedirect.com\/science\/article\/pii\/S2214209622000183","DOI":"10.1016\/j.vehcom.2022.100471"},{"key":"5496_CR13","unstructured":"Mikhail, B., Partha, N., Sindhwani, V.: Manifold regularization: a geometric framework for learning from examples. J. Mach. Learn. Res. 7, 2399\u20132434 (2006)"},{"key":"5496_CR14","unstructured":"Nichol, A., Schulman, J.: Reptile: a scalable metalearning algorithm. arXiv: Learning (2018)"},{"key":"5496_CR15","unstructured":"Perich, D.P., Maci, J.R.S.V., Aparicio, A.C., et\u00a0al.: Unveiling the potential of graph neural networks for robust intrusion detection. 3rd International Workshop on AI in Networks and Distributed Systems (2021)"},{"key":"5496_CR16","doi-asserted-by":"crossref","unstructured":"Sharafaldin, I., Hakak, A.H.L.S., Ghorbani, A.A.: Developing realistic distributed denial of service (ddos) attack dataset and taxonomy. International Carnahan Conference on Security Technology (ICCST) (2019)","DOI":"10.1109\/CCST.2019.8888419"},{"key":"5496_CR17","unstructured":"Xiaojin, Z, Zoubin, G.: Learning from labeled and unlabeled data with label propagation. Technical Report CMU-CALD-02-107, Carnegie Mellon University (2002)"},{"key":"5496_CR18","unstructured":"Yalu, W., Jie, L., Wei, Z., et\u00a0al.: N-stgat: Spatio-temporal graph neural network based network intrusion detection for near-earth remote sensing. Remote Sens (2023)"},{"key":"5496_CR19","doi-asserted-by":"crossref","unstructured":"Yoo, D., Kweon, I.S.: Learning loss for active learning. IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2019)","DOI":"10.1109\/CVPR.2019.00018"},{"key":"5496_CR20","unstructured":"Zonghan, W., Shirui, P., Fengwen, C., et al.: A comprehensive survey on graph neural networks. Machine learning, Arxiv (2019)"}],"container-title":["Cluster Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10586-025-05496-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10586-025-05496-6\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10586-025-05496-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,31]],"date-time":"2025-10-31T15:36:20Z","timestamp":1761924980000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10586-025-05496-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9,15]]},"references-count":20,"journal-issue":{"issue":"12","published-print":{"date-parts":[[2025,11]]}},"alternative-id":["5496"],"URL":"https:\/\/doi.org\/10.1007\/s10586-025-05496-6","relation":{},"ISSN":["1386-7857","1573-7543"],"issn-type":[{"type":"print","value":"1386-7857"},{"type":"electronic","value":"1573-7543"}],"subject":[],"published":{"date-parts":[[2025,9,15]]},"assertion":[{"value":"29 November 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"24 March 2025","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 May 2025","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"15 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 Conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"780"}}