{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,23]],"date-time":"2025-07-23T12:46:16Z","timestamp":1753274776241,"version":"3.38.0"},"reference-count":23,"publisher":"Tech Science Press","issue":"1","license":[{"start":{"date-parts":[[2024,7,19]],"date-time":"2024-07-19T00:00:00Z","timestamp":1721347200000},"content-version":"vor","delay-in-days":200,"URL":"https:\/\/doi.org\/10.32604\/TSP-CROSSMARKPOLICY"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["CMC"],"published-print":{"date-parts":[[2024]]},"DOI":"10.32604\/cmc.2024.051307","type":"journal-article","created":{"date-parts":[[2024,7,9]],"date-time":"2024-07-09T09:18:58Z","timestamp":1720516738000},"page":"163-193","update-policy":"https:\/\/doi.org\/10.32604\/tsp-crossmarkpolicy","source":"Crossref","is-referenced-by-count":1,"title":["Federated Network Intelligence Orchestration for Scalable and Automated FL-Based Anomaly Detection in B5G Networks"],"prefix":"10.32604","volume":"80","author":[{"given":"Pablo Fern\u00e1ndez","family":"Saura","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jos\u00e9 M. Bernab\u00e9","family":"Murcia","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Emilio Garc\u00eda de la Calera","family":"Molina","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Alejandro Molina","family":"Zarca","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jorge Bernal","family":"Bernab\u00e9","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Antonio F. Skarmeta","family":"G\u00f3mez","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"17807","published-online":{"date-parts":[[2024]]},"reference":[{"key":"ref1","series-title":"Proc. 20th Int. Conf. Artif. Intell. Stat.","first-page":"1273","article-title":"Communication-efficient learning of deep networks from decentralized data","volume":"54","author":"McMahan","year":"Apr. 2017"},{"key":"ref2","doi-asserted-by":"crossref","first-page":"20","DOI":"10.1186\/s42400-019-0038-7","article-title":"Survey of intrusion detection systems: Techniques, datasets and challenges","volume":"2","author":"Khraisat","year":"Jul. 2019","journal-title":"Cybersecurity"},{"key":"ref3","first-page":"1","article-title":"Multi-stage optimized machine learning framework for network intrusion detection","author":"Injadat","year":"Aug. 2020","journal-title":"IEEE Trans. Netw. Serv. Manag."},{"key":"ref4","doi-asserted-by":"crossref","first-page":"7700","DOI":"10.1109\/ACCESS.2018.2803446","article-title":"A self-adaptive deep learning-based system for anomaly detection in 5G networks","volume":"6","author":"Fern\u00e1ndez-Maim\u00f3","year":"2018","journal-title":"IEEE Access"},{"key":"ref5","doi-asserted-by":"crossref","unstructured":"A. Thantharate, \u201cFED6G: Federated chameleon learning for network slice management in beyond 5G systems,\u201d presented at the Proc. 2022 IEEE 13th Ann. Inf. Technol. Electron. Mobile Commun. Conf. (IEMCON), Oct. 12\u201315, 2022, pp. 19\u201325. doi: 10.1109\/IEMCON56893.2022.9946488.","DOI":"10.1109\/IEMCON56893.2022.9946488"},{"key":"ref6","doi-asserted-by":"crossref","first-page":"5125","DOI":"10.1109\/TVT.2022.3225087","article-title":"FedRelay: Federated relay learning for 6G mobile edge intelligence","volume":"72","author":"Li","year":"2023","journal-title":"IEEE Trans. Veh. Technol."},{"key":"ref7","first-page":"1","article-title":"ZSM-based E2E security slice management for DDoS attack protection in MEC-enabled V2X environments","author":"Asensio-Garriga","year":"Jan. 2024","journal-title":"IEEE Open J. Veh. Technol."},{"key":"ref8","doi-asserted-by":"crossref","first-page":"131779","DOI":"10.1109\/ACCESS.2020.3010209","article-title":"Security orchestration and enforcement in NFV\/SDN-aware UAV deployments","volume":"8","author":"Hermosilla","year":"2020","journal-title":"IEEE Access"},{"key":"ref9","doi-asserted-by":"crossref","first-page":"108661","DOI":"10.1016\/j.comnet.2021.108661","article-title":"Evaluating FL for intrusion detection in Internet of Things: Review and challenges","volume":"203","author":"M\u00e1rmol-Campos","year":"2022","journal-title":"Comput. Netw."},{"key":"ref10","doi-asserted-by":"crossref","first-page":"121000","DOI":"10.1016\/j.eswa.2023.121000","article-title":"Fed-ANIDS: FL for anomaly-based network intrusion detection systems","volume":"234","author":"Idrissi","year":"2023","journal-title":"Expert Syst. Appl."},{"key":"ref11","doi-asserted-by":"crossref","first-page":"1342","DOI":"10.1109\/COMST.2021.3058573","article-title":"Federated machine learning: Survey, multi-level classification, desirable criteria and future directions in communication and networking systems","volume":"23","author":"Wahab","year":"2021","journal-title":"IEEE Commun. Surv. Tutor."},{"key":"ref12","doi-asserted-by":"crossref","first-page":"250","DOI":"10.1016\/j.future.2023.06.001","article-title":"Survey on federated learning enabling indoor navigation for industry 4.0 in B5G","volume":"148","author":"Alsamhi","year":"2023","journal-title":"Future Gener. Comput. Syst."},{"key":"ref13","doi-asserted-by":"crossref","first-page":"7468","DOI":"10.1109\/TII.2022.3170900","article-title":"DRL-based joint resource allocation and device orchestration for hierarchical FL in NOMA-enabled industrial IoT","volume":"19","author":"Zhao","year":"2022","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref14","doi-asserted-by":"crossref","unstructured":"M. Camelo et al., \u201cRequirements and specifications for the orchestration of network intelligence in 6G,\u201d presented at the 2022 IEEE 19th Annu. Consum. Commun. Netw. Conf. (CCNC), Jan. 8\u201311, 2022, pp. 1\u20139. doi: 10.1109\/CCNC49033.2022.9700729.","DOI":"10.1109\/CCNC49033.2022.9700729"},{"key":"ref15","doi-asserted-by":"crossref","unstructured":"S. D\u2019Oro, L. Bonati, M. Polese, and T. Melodia, \u201cOrchestRAN: Network automation through orchestrated intelligence in the open RAN,\u201d presented at the IEEE INFOCOM 2022\u2014IEEE Conf. Comput. Commun., May 2\u20135, 2022, pp. 270\u2013279. doi: 10.1109\/INFOCOM48880.2022.9796744.","DOI":"10.1109\/INFOCOM48880.2022.9796744"},{"key":"ref16","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1109\/MNET.001.2000047","article-title":"AI-based autonomous control, management, and orchestration in 5G: From standards to algorithms","volume":"34","author":"Bega","year":"2020","journal-title":"IEEE Netw."},{"key":"ref17","doi-asserted-by":"crossref","first-page":"1145","DOI":"10.1109\/TII.2021.3126728","article-title":"Intrusion detection based on privacy-preserving FL for the industrial IoT","volume":"19","author":"Ruzafa-Alc\u00e1zar","year":"2021","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref18","unstructured":"S. Samarakoon et al., \u201c5G-NIDD: A comprehensive network intrusion detection dataset generated over 5G wireless network,\u201d IEEE Dataport, 2022. doi: 10.21227\/xtep-hv36."},{"key":"ref19","doi-asserted-by":"crossref","first-page":"1690","DOI":"10.1016\/j.eswa.2013.08.066","article-title":"A novel hybrid intrusion detection method integrating anomaly detection with misuse detection","volume":"41","author":"Kim","year":"2014","journal-title":"Expert Syst. Appl."},{"key":"ref20","doi-asserted-by":"crossref","DOI":"10.1186\/s40537-021-00498-8","article-title":"A novel time efficient learning-based approach for smart intrusion detection system","volume":"8","author":"Seth","year":"Aug. 2021","journal-title":"J. Big Data"},{"key":"ref21","first-page":"102419","article-title":"Deep learning for cyber security intrusion detection: Approaches, datasets, and comparative study","volume":"50","author":"Ferrag","year":"2020","journal-title":"J. Inf. Secur. Appl."},{"key":"ref22","doi-asserted-by":"crossref","unstructured":"S. I. Kim and H. S. Kim, \u201cA VNF placement method based on VNF characteristics,\u201d presented at the 2021 Int. Conf. Inf. Netw. (ICOIN), Jeju Island, Republic of Korea, Jan. 13\u201316, 2021, pp. 864\u2013869. doi: 10.1109\/ICOIN50884.2021.","DOI":"10.1109\/ICOIN50884.2021.9334022"},{"key":"ref23","doi-asserted-by":"crossref","first-page":"104","DOI":"10.1109\/TNSM.2020.3044329","article-title":"Capacitated shortest path tour problem-based integer linear programming for service chaining and function placement in NFV networks","volume":"18","author":"Sasabe","year":"Mar. 2021","journal-title":"IEEE Trans. Netw. Serv. Manag."}],"container-title":["Computers, Materials & Continua"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.techscience.com\/files\/cmc\/2024\/TSP_CMC-80-1\/TSP_CMC_51307\/TSP_CMC_51307.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,3,6]],"date-time":"2025-03-06T11:35:18Z","timestamp":1741260918000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.techscience.com\/cmc\/v80n1\/57382"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"references-count":23,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2024]]},"published-print":{"date-parts":[[2024]]}},"URL":"https:\/\/doi.org\/10.32604\/cmc.2024.051307","relation":{},"ISSN":["1546-2226"],"issn-type":[{"type":"electronic","value":"1546-2226"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"2024-03-01","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2024-05-20","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2024-07-18","order":2,"name":"published","label":"Published Online","group":{"name":"publication_history","label":"Publication History"}}]}}