{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,2]],"date-time":"2026-04-02T07:01:37Z","timestamp":1775113297903,"version":"3.50.1"},"reference-count":21,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2026,4,2]],"date-time":"2026-04-02T00:00:00Z","timestamp":1775088000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,4,2]],"date-time":"2026-04-02T00:00:00Z","timestamp":1775088000000},"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":["J Comput Virol Hack Tech"],"DOI":"10.1007\/s11416-026-00616-1","type":"journal-article","created":{"date-parts":[[2026,4,2]],"date-time":"2026-04-02T06:39:49Z","timestamp":1775111989000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["FP-LSTM: a federated proximal LSTM autoencoder framework for zero-day cyberattack detection"],"prefix":"10.1007","volume":"22","author":[{"given":"Peda Gopi","family":"Arepalli","sequence":"first","affiliation":[]},{"given":"Karuna Sri","family":"Jaladi","sequence":"additional","affiliation":[]},{"given":"Jairam Naik","family":"Khetavath","sequence":"additional","affiliation":[]},{"given":"Chaitanya","family":"Kosaraju","sequence":"additional","affiliation":[]},{"given":"Satya Sandeep","family":"Kanumalli","sequence":"additional","affiliation":[]},{"given":"Lakshman Narayana","family":"Vejendla","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2026,4,2]]},"reference":[{"issue":"9","key":"616_CR1","doi-asserted-by":"publisher","first-page":"13278","DOI":"10.1109\/TVT.2024.3392793","volume":"73","author":"T Li","year":"2024","unstructured":"Li, T., Hong, Z., Feng, W., Yu, L., Wen, Z.: Ms-zerowall: detecting zero-day multi-step attack in smart home using vae and hmm. IEEE Trans Vehicul Technol 73(9), 13278\u201313291 (2024)","journal-title":"IEEE Trans Vehicul Technol"},{"key":"616_CR2","doi-asserted-by":"publisher","first-page":"104951","DOI":"10.1016\/j.jpdc.2024.104951","volume":"193","author":"R Shrestha","year":"2024","unstructured":"Shrestha, R., Mohammadi, M., Sinaei, S., Salcines, A., Pampliega, D., Clemente, R., et al.: Anomaly detection based on LSTM and autoencoders using federated learning in smart electric grid. J Parallel Distrib Comput 193, 104951 (2024)","journal-title":"J Parallel Distrib Comput"},{"key":"616_CR3","doi-asserted-by":"publisher","first-page":"111023","DOI":"10.1016\/j.comnet.2024.111023","volume":"258","author":"A Belenguer","year":"2025","unstructured":"Belenguer, A., Pascual, J.A., Navaridas, J.: A review of federated learning applications in intrusion detection systems. Comput Netw 258, 111023 (2025)","journal-title":"Comput Netw"},{"key":"616_CR4","first-page":"200553","volume":"27","author":"M Devi","year":"2025","unstructured":"Devi, M., Nandal, P., Sehrawat, H.: Federated learning-enabled lightweight intrusion detection system for wireless sensor networks: a cybersecurity approach against DDoS attacks in smart city environments. Intell Syst Appl 27, 200553 (2025)","journal-title":"Intell Syst Appl"},{"issue":"1","key":"616_CR5","doi-asserted-by":"publisher","first-page":"2334303","DOI":"10.1080\/21642583.2024.2334303","volume":"12","author":"MM Aslam","year":"2024","unstructured":"Aslam, M.M., Tufail, A., De Silva, L.C., Haji Mohd Apong, R.A.A., Namoun, A.: An improved autoencoder-based approach for anomaly detection in industrial control systems. Syst Sci Control Eng 12(1), 2334303 (2024)","journal-title":"Syst Sci Control Eng"},{"issue":"6","key":"616_CR6","doi-asserted-by":"publisher","first-page":"205","DOI":"10.3390\/computers14060205","volume":"14","author":"V Babaey","year":"2025","unstructured":"Babaey, V., Faragardi, H.R.: Detecting zero-day web attacks with an ensemble of LSTM, GRU, and stacked autoencoders. Computers 14(6), 205 (2025)","journal-title":"Computers"},{"issue":"3","key":"616_CR7","doi-asserted-by":"publisher","first-page":"573","DOI":"10.3390\/electronics12030573","volume":"12","author":"B Ibrahim Hairab","year":"2023","unstructured":"Ibrahim Hairab, B., Aslan, H.K., Elsayed, M.S., Jurcut, A.D., Azer, M.A.: Anomaly detection of zero-day attacks based on CNN and regularization techniques. Electronics 12(3), 573 (2023)","journal-title":"Electronics"},{"issue":"5","key":"616_CR8","doi-asserted-by":"publisher","first-page":"3930","DOI":"10.1109\/JIOT.2021.3100755","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 Internet Things J 9(5), 3930\u20133944 (2021)","journal-title":"IEEE Internet Things J"},{"key":"616_CR9","doi-asserted-by":"publisher","first-page":"53854","DOI":"10.1109\/ACCESS.2022.3173288","volume":"10","author":"TT Huong","year":"2022","unstructured":"Huong, T.T., Bac, T.P., Ha, K.N., Hoang, N.V., Hoang, N.X., Hung, N.T., Tran, K.P.: Federated learning-based explainable anomaly detection for industrial control systems. IEEE Access 10, 53854\u201353872 (2022)","journal-title":"IEEE Access"},{"key":"616_CR10","doi-asserted-by":"crossref","unstructured":"Korba, A.A., Boualouache, A., Brik, B., Rahal, R., Ghamri-Doudane, Y., Senouci, S. M. (2023). Federated learning for zero-day attack detection in 5g and beyond v2x networks. In:\u00a0ICC 2023-IEEE International Conference on Communications\u00a0(pp. 1137\u20131142). IEEE.","DOI":"10.1109\/ICC45041.2023.10279368"},{"issue":"2","key":"616_CR11","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s10207-025-01000-8","volume":"24","author":"EM Campos","year":"2025","unstructured":"Campos, E.M., Gonzalez-Vidal, A., Hernandez-Ramos, J.L., Skarmeta, A.: Federated learning for misbehaviour detection with variational autoencoders and Gaussian mixture models. Int J Inf Sec 24(2), 1\u201316 (2025)","journal-title":"Int J Inf Sec"},{"key":"616_CR12","doi-asserted-by":"crossref","unstructured":"Chaurasia, N., Ram, M., Verma, P., Mehta, N., Bharot, N. (2024). A federated learning approach to network intrusion detection using residual networks in industrial IoT networks.","DOI":"10.1007\/s11227-024-06153-2"},{"issue":"1","key":"616_CR13","doi-asserted-by":"publisher","first-page":"236","DOI":"10.1007\/s44196-024-00644-z","volume":"17","author":"A Pinto","year":"2024","unstructured":"Pinto, A., Herrera, L.C., Donoso, Y., Gutierrez, J.A.: Enhancing critical infrastructure security: unsupervised learning approaches for anomaly detection. Int J Comput Intell Syst 17(1), 236 (2024)","journal-title":"Int J Comput Intell Syst"},{"issue":"10","key":"616_CR14","doi-asserted-by":"publisher","first-page":"3218","DOI":"10.3390\/s24103218","volume":"24","author":"T Ohtani","year":"2024","unstructured":"Ohtani, T., Yamamoto, R., Ohzahata, S.: IDAC: federated learning-based intrusion detection using autonomously extracted anomalies in IoT. Sensors 24(10), 3218 (2024)","journal-title":"Sensors"},{"issue":"10","key":"616_CR15","doi-asserted-by":"publisher","first-page":"3236","DOI":"10.3390\/s24103236","volume":"24","author":"A Alshehri","year":"2024","unstructured":"Alshehri, A., Badr, M.M., Baza, M., Alshahrani, H.: Deep anomaly detection framework utilizing federated learning for electricity theft zero-day cyberattacks. Sensors 24(10), 3236 (2024)","journal-title":"Sensors"},{"issue":"10","key":"616_CR16","doi-asserted-by":"publisher","first-page":"3043","DOI":"10.3390\/s25103043","volume":"25","author":"A Deshmukh","year":"2025","unstructured":"Deshmukh, A., de la Rosa, P.E., Rodriguez, R.V., Dasari, S.: Enhancing privacy in IoT-enabled digital infrastructure: evaluating federated learning for intrusion and fraud detection. Sensors 25(10), 3043 (2025)","journal-title":"Sensors"},{"issue":"13","key":"616_CR17","doi-asserted-by":"publisher","first-page":"4111","DOI":"10.3390\/s25134111","volume":"25","author":"AH Massarani","year":"2025","unstructured":"Massarani, A.H., Badr, M.M., Baza, M., Alshahrani, H., Alshehri, A.: Efficient and accurate zero-day electricity theft detection from smart meter sensor data using prototype and ensemble learning. Sensors 25(13), 4111 (2025)","journal-title":"Sensors"},{"issue":"1","key":"616_CR18","doi-asserted-by":"publisher","first-page":"3856","DOI":"10.1109\/TCE.2023.3335385","volume":"70","author":"P Verma","year":"2023","unstructured":"Verma, P., Bharot, N., Breslin, J.G., O\u2019Shea, D., Vidyarthi, A., Gupta, D.: Zero-day guardian: a dual model enabled federated learning framework for handling zero-day attacks in 5G enabled IIoT. IEEE Trans Consum Electron 70(1), 3856\u20133866 (2023)","journal-title":"IEEE Trans Consum Electron"},{"issue":"5","key":"616_CR19","doi-asserted-by":"publisher","first-page":"2211","DOI":"10.1007\/s40747-021-00396-9","volume":"7","author":"V Kumar","year":"2021","unstructured":"Kumar, V., Sinha, D.: A robust intelligent zero-day cyber-attack detection technique. Complex Intell Syst 7(5), 2211\u20132234 (2021)","journal-title":"Complex Intell Syst"},{"issue":"4","key":"616_CR20","doi-asserted-by":"publisher","first-page":"947","DOI":"10.1007\/s10207-023-00676-0","volume":"22","author":"M Sarhan","year":"2023","unstructured":"Sarhan, M., Layeghy, S., Gallagher, M., Portmann, M.: From zero-shot machine learning to zero-day attack detection. Int J Inf Sec 22(4), 947\u2013959 (2023)","journal-title":"Int J Inf Sec"},{"key":"616_CR21","unstructured":"Dr. Mike Wa Nkongolo. UGRansome . Kaggle, https:\/\/www.kaggle.com\/datasets\/nkongolo\/ugransome-dataset (Accessed on December 14,2023)"}],"container-title":["Journal of Computer Virology and Hacking Techniques"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11416-026-00616-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11416-026-00616-1","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11416-026-00616-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,2]],"date-time":"2026-04-02T06:39:52Z","timestamp":1775111992000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11416-026-00616-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,4,2]]},"references-count":21,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,12]]}},"alternative-id":["616"],"URL":"https:\/\/doi.org\/10.1007\/s11416-026-00616-1","relation":{},"ISSN":["2263-8733"],"issn-type":[{"value":"2263-8733","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,4,2]]},"assertion":[{"value":"17 December 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"11 March 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 April 2026","order":3,"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 competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}},{"value":"Author seen and agreed with the contents of the manuscript and are looking forward to publishing this paper in this journal.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}},{"value":"Author gave explicit consent to publish this\u00a0manuscript.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}}],"article-number":"34"}}