{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,21]],"date-time":"2026-05-21T22:26:18Z","timestamp":1779402378487,"version":"3.53.1"},"publisher-location":"Cham","reference-count":22,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031569494","type":"print"},{"value":"9783031569500","type":"electronic"}],"license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"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":[[2024]]},"DOI":"10.1007\/978-3-031-56950-0_18","type":"book-chapter","created":{"date-parts":[[2024,3,28]],"date-time":"2024-03-28T03:01:57Z","timestamp":1711594917000},"page":"206-218","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["A Federated Learning Anomaly Detection Approach for\u00a0IoT Environments"],"prefix":"10.1007","author":[{"given":"Basem","family":"Suleiman","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ali","family":"Anaissi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wenbo","family":"Yan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Abubakar","family":"Bello","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sophie","family":"Zou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ling Nga Meric","family":"Tong","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,3,29]]},"reference":[{"key":"18_CR1","unstructured":"Uci mchine learning repository: detection_of_iot_botnet_attacks_n_baiot data set. http:\/\/archive.ics.uci.edu\/ml\/datasets\/detection_of_IoT_botnet_attacks_N_BaIoT"},{"issue":"6","key":"18_CR2","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3230708","volume":"9","author":"A Anaissi","year":"2018","unstructured":"Anaissi, A., Khoa, N.L.D., Rakotoarivelo, T., Alamdari, M.M., Wang, Y.: Adaptive online one-class support vector machines with applications in structural health monitoring. ACM Trans. Intell. Syst. Technol. (TIST) 9(6), 1\u201320 (2018)","journal-title":"ACM Trans. Intell. Syst. Technol. (TIST)"},{"issue":"1","key":"18_CR3","doi-asserted-by":"publisher","first-page":"91","DOI":"10.1007\/s13349-019-00323-0","volume":"9","author":"A Anaissi","year":"2019","unstructured":"Anaissi, A., Khoa, N.L.D., Rakotoarivelo, T., Alamdari, M.M., Wang, Y.: Smart pothole detection system using vehicle-mounted sensors and machine learning. J. Civ. Struct. Heal. Monit. 9(1), 91\u2013102 (2019)","journal-title":"J. Civ. Struct. Heal. Monit."},{"key":"18_CR4","doi-asserted-by":"crossref","unstructured":"Anaissi, A., Suleiman, B., Alyassine, W.: Personalised federated learning framework for damage detection in structural health monitoring. J. Civil Struct. Health Monit. 1\u201314 (2022)","DOI":"10.1007\/s13349-022-00615-y"},{"key":"18_CR5","doi-asserted-by":"publisher","unstructured":"Anaissi, A., Suleiman, B., Alyassine, W.: A personalized federated learning algorithm for one-class support vector machine: an application in anomaly detection. In: Groen, D., de Mulatier, C., Paszynski, M., Krzhizhanovskaya, V.V., Dongarra, J.J., Sloot, P.M.A. (eds.) ICCS 2022, pp. 373\u2013379. Springer, Heidelberg (2022). https:\/\/doi.org\/10.1007\/978-3-031-08760-8_31","DOI":"10.1007\/978-3-031-08760-8_31"},{"key":"18_CR6","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"155","DOI":"10.1007\/978-3-030-74251-5_13","volume-title":"Advances in Intelligent Data Analysis XIX","author":"A Anaissi","year":"2021","unstructured":"Anaissi, A., Suleiman, B., Naji, M.: Intelligent structural damage detection: a federated learning approach. In: Abreu, P.H., Rodrigues, P.P., Fern\u00e1ndez, A., Gama, J. (eds.) IDA 2021. LNCS, vol. 12695, pp. 155\u2013170. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-74251-5_13"},{"issue":"6","key":"18_CR7","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3451217","volume":"15","author":"A Anaissi","year":"2021","unstructured":"Anaissi, A., Suleiman, B., Zandavi, S.M.: Online tensor-based learning model for structural damage detection. ACM Trans. Knowl. Disc. Data (TKDD) 15(6), 1\u201318 (2021)","journal-title":"ACM Trans. Knowl. Disc. Data (TKDD)"},{"key":"18_CR8","doi-asserted-by":"crossref","unstructured":"Eskin, E., Arnold, A., Prerau, M., Portnoy, L., Stolfo, S.: A geometric framework for unsupervised anomaly detection: detecting intrusions in unlabeled data. Appl. Data Mining Comput. Secur. 6 (2002)","DOI":"10.1007\/978-1-4615-0953-0_4"},{"key":"18_CR9","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"170","DOI":"10.1007\/3-540-46145-0_17","volume-title":"Data Warehousing and Knowledge Discovery","author":"S Hawkins","year":"2002","unstructured":"Hawkins, S., He, H., Williams, G., Baxter, R.: Outlier detection using replicator neural networks. In: Kambayashi, Y., Winiwarter, W., Arikawa, M. (eds.) DaWaK 2002. LNCS, vol. 2454, pp. 170\u2013180. Springer, Heidelberg (2002). https:\/\/doi.org\/10.1007\/3-540-46145-0_17"},{"key":"18_CR10","doi-asserted-by":"crossref","unstructured":"Khoa, N.L.D., Anaissi, A., Wang, Y.: Smart infrastructure maintenance using incremental tensor analysis. In: Proceedings of the 2017 ACM on Conference on Information and Knowledge Management, pp. 959\u2013967 (2017)","DOI":"10.1145\/3132847.3132851"},{"key":"18_CR11","unstructured":"Kone\u010dn\u1ef3, J., McMahan, H.B., Ramage, D., Richt\u00e1rik, P.: Federated optimization: distributed machine learning for on-device intelligence. arXiv preprint arXiv:1610.02527 (2016)"},{"key":"18_CR12","unstructured":"Li, S., Cheng, Y., Liu, Y., Wang, W., Chen, T.: Abnormal client behavior detection in federated learning. arXiv preprint arXiv:1910.09933 (2019)"},{"issue":"8","key":"18_CR13","doi-asserted-by":"publisher","first-page":"6348","DOI":"10.1109\/JIOT.2020.3011726","volume":"8","author":"Y Liu","year":"2020","unstructured":"Liu, Y., et al.: Deep anomaly detection for time-series data in industrial IoT: a communication-efficient on-device federated learning approach. IEEE Internet Things J. 8(8), 6348\u20136358 (2020)","journal-title":"IEEE Internet Things J."},{"key":"18_CR14","unstructured":"McMahan, H.B., Moore, E., Ramage, D., Arcas, B.A.: Federated learning of deep networks using model averaging. arXiv preprint arXiv:1602.05629 (2016)"},{"key":"18_CR15","unstructured":"McMahan, H.B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data (2017)"},{"issue":"3","key":"18_CR16","doi-asserted-by":"publisher","first-page":"12","DOI":"10.1109\/mprv.2018.03367731","volume":"17","author":"Y Meidan","year":"2018","unstructured":"Meidan, Y., et al.: N-baiot-network-based detection of IoT botnet attacks using deep autoencoders. IEEE Pervasive Comput. 17(3), 12\u201322 (2018). https:\/\/doi.org\/10.1109\/mprv.2018.03367731","journal-title":"IEEE Pervasive Comput."},{"key":"18_CR17","doi-asserted-by":"crossref","unstructured":"Mirsky, Y., Doitshman, T., Elovici, Y., Shabtai, A.: Kitsune: an ensemble of autoencoders for online network intrusion detection (2018)","DOI":"10.14722\/ndss.2018.23204"},{"key":"18_CR18","doi-asserted-by":"publisher","unstructured":"Preuveneers, D., Rimmer, V., Tsingenopoulos, I., Spooren, J., Joosen, W., Ilie-Zudor, E.: Chained anomaly detection models for federated learning: an intrusion detection case study. Appl. Sci. 8(12) (2018). https:\/\/doi.org\/10.3390\/app8122663. https:\/\/www.mdpi.com\/2076-3417\/8\/12\/2663","DOI":"10.3390\/app8122663"},{"key":"18_CR19","doi-asserted-by":"crossref","unstructured":"Sater, R.A., Hamza, A.B.: A federated learning approach to anomaly detection in smart buildings (2020)","DOI":"10.1145\/3467981"},{"issue":"3","key":"18_CR20","doi-asserted-by":"publisher","first-page":"2180","DOI":"10.1109\/TAES.2021.3134751","volume":"58","author":"SM Zandavi","year":"2022","unstructured":"Zandavi, S.M., Chung, V., Anaissi, A.: Accelerated control using stochastic dual simplex algorithm and genetic filter for drone application. IEEE Trans. Aerosp. Electron. Syst. 58(3), 2180\u20132191 (2022). https:\/\/doi.org\/10.1109\/TAES.2021.3134751","journal-title":"IEEE Trans. Aerosp. Electron. Syst."},{"issue":"5","key":"18_CR21","doi-asserted-by":"publisher","first-page":"2725","DOI":"10.1109\/TCYB.2019.2931288","volume":"51","author":"SM Zandavi","year":"2019","unstructured":"Zandavi, S.M., Chung, V.Y.Y., Anaissi, A.: Stochastic dual simplex algorithm: a novel heuristic optimization algorithm. IEEE Trans. Cybern. 51(5), 2725\u20132734 (2019)","journal-title":"IEEE Trans. Cybern."},{"issue":"5","key":"18_CR22","doi-asserted-by":"publisher","first-page":"4275","DOI":"10.1109\/TVT.2019.2907269","volume":"68","author":"K Zhu","year":"2019","unstructured":"Zhu, K., Chen, Z., Peng, Y., Zhang, L.: Mobile edge assisted literal multi-dimensional anomaly detection of in-vehicle network using LSTM. IEEE Trans. Veh. Technol. 68(5), 4275\u20134284 (2019)","journal-title":"IEEE Trans. Veh. Technol."}],"container-title":["Lecture Notes in Networks and Systems","Proceedings of the Second International Conference on Advances in Computing Research (ACR\u201924)"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-56950-0_18","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,28]],"date-time":"2024-03-28T03:12:28Z","timestamp":1711595548000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-56950-0_18"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9783031569494","9783031569500"],"references-count":22,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-56950-0_18","relation":{},"ISSN":["2367-3370","2367-3389"],"issn-type":[{"value":"2367-3370","type":"print"},{"value":"2367-3389","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"29 March 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ACR","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Advances in Computing Research","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Madrid","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Spain","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":"3 June 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"5 June 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"acr2023a","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/iicser.org\/ACR24","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}