{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,17]],"date-time":"2026-03-17T19:27:53Z","timestamp":1773775673110,"version":"3.50.1"},"publisher-location":"Singapore","reference-count":23,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819785391","type":"print"},{"value":"9789819785407","type":"electronic"}],"license":[{"start":{"date-parts":[[2024,10,18]],"date-time":"2024-10-18T00:00:00Z","timestamp":1729209600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,10,18]],"date-time":"2024-10-18T00:00:00Z","timestamp":1729209600000},"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":[],"published-print":{"date-parts":[[2025]]},"DOI":"10.1007\/978-981-97-8540-7_16","type":"book-chapter","created":{"date-parts":[[2024,10,17]],"date-time":"2024-10-17T10:01:56Z","timestamp":1729159316000},"page":"260-275","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Securing Networks: A Deep Learning Approach with\u00a0Explainable AI (XAI) and\u00a0Federated Learning for\u00a0Intrusion Detection"],"prefix":"10.1007","author":[{"given":"Kazi","family":"Fatema","sequence":"first","affiliation":[]},{"given":"Mehrin","family":"Anannya","sequence":"additional","affiliation":[]},{"given":"Samrat Kumar","family":"Dey","sequence":"additional","affiliation":[]},{"given":"Chunhua","family":"Su","sequence":"additional","affiliation":[]},{"given":"Rashed","family":"Mazumder","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,10,18]]},"reference":[{"key":"16_CR1","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1023\/A:1010933404324","volume":"45","author":"L Breiman","year":"2001","unstructured":"Breiman, L.: Random forests. Mach. Learn. 45, 5\u201332 (2001)","journal-title":"Mach. Learn."},{"key":"16_CR2","doi-asserted-by":"crossref","unstructured":"Breiman, L.: Classification and Regression Trees. Routledge (2017)","DOI":"10.1201\/9781315139470"},{"key":"16_CR3","unstructured":"Ke, G., et al.: LightGBM: a highly efficient gradient boosting decision tree. In: Advances in Neural Information Processing Systems, vol. 30 (2017)"},{"key":"16_CR4","doi-asserted-by":"crossref","unstructured":"Chen, T., Guestrin, C.: XGBoost: a scalable tree boosting system. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 785\u2013794 (2016)","DOI":"10.1145\/2939672.2939785"},{"issue":"13","key":"16_CR5","doi-asserted-by":"publisher","first-page":"5941","DOI":"10.3390\/s23135941","volume":"23","author":"ECP Neto","year":"2023","unstructured":"Neto, E.C.P., Dadkhah, S., Ferreira, R., Zohourian, A., Lu, R., Ghorbani, A.A.: CICIoT2023: a real-time dataset and benchmark for large-scale attacks in IoT environment. Sensors 23(13), 5941 (2023)","journal-title":"Sensors"},{"key":"16_CR6","unstructured":"Vaswani, A., et al.: Attention is all you need. In: Advances in Neural Information Processing Systems, vol. 30 (2017)"},{"key":"16_CR7","unstructured":"McMahan, B., Moore, E., Ramage, D., Hampson, S., y Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273\u20131282. PMLR (2017)"},{"key":"16_CR8","doi-asserted-by":"publisher","first-page":"52138","DOI":"10.1109\/ACCESS.2018.2870052","volume":"6","author":"A Adadi","year":"2018","unstructured":"Adadi, A., Berrada, M.: Peeking inside the black-box: a survey on explainable artificial intelligence (XAI). IEEE Access 6, 52138\u201352160 (2018)","journal-title":"IEEE Access"},{"key":"16_CR9","doi-asserted-by":"publisher","first-page":"101845","DOI":"10.1016\/j.compenvurbsys.2022.101845","volume":"96","author":"Z Li","year":"2022","unstructured":"Li, Z.: Extracting spatial effects from machine learning model using local interpretation method: an example of SHAP and XGBoost. Comput. Environ. Urban Syst. 96, 101845 (2022)","journal-title":"Comput. Environ. Urban Syst."},{"key":"16_CR10","series-title":"IFIP Advances in Information and Communication Technology","doi-asserted-by":"publisher","first-page":"132","DOI":"10.1007\/978-3-031-08333-4_11","volume-title":"Artificial Intelligence Applications and Innovations","author":"T Markovic","year":"2022","unstructured":"Markovic, T., Leon, M., Buffoni, D., Punnekkat, S.: Random forest based on federated learning for intrusion detection. In: Maglogiannis, I., Iliadis, L., Macintyre, J., Cortez, P. (eds.) AIAI 2022. IFIP Advances in Information and Communication Technology, vol. 646, pp. 132\u2013144. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-031-08333-4_11"},{"issue":"3","key":"16_CR11","doi-asserted-by":"publisher","first-page":"509","DOI":"10.3390\/ai4030028","volume":"4","author":"R Lazzarini","year":"2023","unstructured":"Lazzarini, R., Tianfield, H., Charissis, V.: Federated learning for IoT intrusion detection. AI 4(3), 509\u2013530 (2023)","journal-title":"AI"},{"issue":"1","key":"16_CR12","doi-asserted-by":"publisher","first-page":"158","DOI":"10.3390\/network3010008","volume":"3","author":"MM Rashid","year":"2023","unstructured":"Rashid, M.M., Khan, S.U., Eusufzai, F., Redwan, M.A., Sabuj, S.R., Elsharief, M.: A federated learning-based approach for improving intrusion detection in industrial internet of things networks. Network 3(1), 158\u2013179 (2023)","journal-title":"Network"},{"key":"16_CR13","doi-asserted-by":"publisher","first-page":"121000","DOI":"10.1016\/j.eswa.2023.121000","volume":"234","author":"MJ Idrissi","year":"2023","unstructured":"Idrissi, M.J., et al.: Fed-ANIDS: federated learning for anomaly-based network intrusion detection systems. Expert Syst. Appl. 234, 121000 (2023)","journal-title":"Expert Syst. Appl."},{"issue":"2","key":"16_CR14","first-page":"2503","volume":"24","author":"W Liu","year":"2022","unstructured":"Liu, W., Xiaolong, X., Wu, L., Qi, L., Jolfaei, A., Ding, W., Khosravi, M.R.: Intrusion detection for maritime transportation systems with batch federated aggregation. IEEE Trans. Intell. Transp. Syst. 24(2), 2503\u20132514 (2022)","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"16_CR15","doi-asserted-by":"crossref","unstructured":"Nasir, M.U., Mehmood, S., Khan, M.A., Zubair, M., Khan, F., Lee, Y.: Network intrusion detection empowered with federated machine learning (2023)","DOI":"10.21203\/rs.3.rs-3350992\/v1"},{"key":"16_CR16","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s44196-021-00047-4","volume":"14","author":"M Al-Imran","year":"2021","unstructured":"Al-Imran, M., Ripon, S.H.: Network intrusion detection: an analytical assessment using deep learning and state-of-the-art machine learning models. Int. J. Comput. Intell. Syst. 14, 1\u201320 (2021)","journal-title":"Int. J. Comput. Intell. Syst."},{"key":"16_CR17","doi-asserted-by":"crossref","unstructured":"Nguyen, S.-N., Nguyen, V.-Q., Choi, J., Kim, K.: Design and implementation of intrusion detection system using convolutional neural network for DoS detection. In: Proceedings of the 2nd International Conference on Machine Learning and Soft Computing, pp. 34\u201338 (2018)","DOI":"10.1145\/3184066.3184089"},{"issue":"4","key":"16_CR18","first-page":"565","volume":"12","author":"RS Arslan","year":"2021","unstructured":"Arslan, R.S.: FastTrafficAnalyzer: an efficient method for intrusion detection systems to analyze network traffic. Dicle \u00dcniversitesi M\u00fchendislik Fak\u00fcltesi M\u00fchendislik Dergisi 12(4), 565\u2013572 (2021)","journal-title":"Dicle \u00dcniversitesi M\u00fchendislik Fak\u00fcltesi M\u00fchendislik Dergisi"},{"key":"16_CR19","doi-asserted-by":"crossref","unstructured":"de Carvalho Bertoli, G., Pereira Junior, L.A., Saotome, O., dos Santos, A.L.: Generalizing intrusion detection for heterogeneous networks: a stacked-unsupervised federated learning approach. Comput. Secur. 127, 103106 (2023)","DOI":"10.1016\/j.cose.2023.103106"},{"issue":"6","key":"16_CR20","doi-asserted-by":"publisher","first-page":"1053","DOI":"10.3390\/electronics13061053","volume":"13","author":"S Yaras","year":"2024","unstructured":"Yaras, S., Dener, M.: IoT-based intrusion detection system using new hybrid deep learning algorithm. Electronics 13(6), 1053 (2024)","journal-title":"Electronics"},{"issue":"2","key":"16_CR21","doi-asserted-by":"publisher","first-page":"32","DOI":"10.3390\/informatics11020032","volume":"11","author":"FL Becerra-Suarez","year":"2024","unstructured":"Becerra-Suarez, F.L., Tuesta-Monteza, V.A., Mejia-Cabrera, H.I., Arcila-Diaz, J.: Performance evaluation of deep learning models for classifying cybersecurity attacks in IoT networks. Informatics 11(2), 32 (2024)","journal-title":"Informatics"},{"issue":"1","key":"16_CR22","doi-asserted-by":"publisher","first-page":"5872","DOI":"10.1038\/s41598-024-56126-x","volume":"14","author":"MM Khan","year":"2024","unstructured":"Khan, M.M., Alkhathami, M.: Anomaly detection in IoT-based healthcare: machine learning for enhanced security. Sci. Rep. 14(1), 5872 (2024)","journal-title":"Sci. Rep."},{"issue":"5","key":"16_CR23","doi-asserted-by":"publisher","first-page":"717","DOI":"10.1016\/S0731-7085(99)00272-1","volume":"22","author":"S Agatonovic-Kustrin","year":"2000","unstructured":"Agatonovic-Kustrin, S., Beresford, R.: Basic concepts of artificial neural network (ANN) modeling and its application in pharmaceutical research. J. Pharmac. Biomed. Anal. 22(5), 717\u2013727 (2000)","journal-title":"J. Pharmac. Biomed. Anal."}],"container-title":["Lecture Notes in Computer Science","Data Security and Privacy Protection"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-97-8540-7_16","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,17]],"date-time":"2024-10-17T10:04:55Z","timestamp":1729159495000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-97-8540-7_16"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10,18]]},"ISBN":["9789819785391","9789819785407"],"references-count":23,"URL":"https:\/\/doi.org\/10.1007\/978-981-97-8540-7_16","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,10,18]]},"assertion":[{"value":"18 October 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"DSPP","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Data Security and Privacy Protection","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Xi'an","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"25 October 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"28 October 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"dspp2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}