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However, today\u2019s IIoT still faces the challenges of modeling varying time-series in common data isolation while considering data security. To accurately characterize industrial dynamics, we propose a possible solution based on federated sequence learning (FSL) with cyber attack detection capabilities. Under a federated framework, FSL constructs a collaborative global model without violating local data integrity. Taking advantages of the locally sequential modeling, FSL captures the intrinsic industrial time-series responses. Furthermore, data heterogeneity among distributed clients is also considered, which is important to maintenance a robust but sensitive attack detection. Experiments on classic distributed datasets demonstrate that FSL is capable to accurately model data heterogeneity caused by data isolation and dynamics of time-series. Real IIoT attack detection experiments using a distributed testbed show that our FSL provides better detection performances for industrial time-series sensory data compared to existing methods. Therefore, the proposed attack detection approach FSL is promising in real IIoT scenarios in terms of feasibility, robustness and accuracy.<\/jats:p>","DOI":"10.1007\/s44244-023-00006-2","type":"journal-article","created":{"date-parts":[[2023,3,17]],"date-time":"2023-03-17T00:03:36Z","timestamp":1679011416000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["FSL: federated sequential learning-based cyberattack detection for Industrial Internet of Things"],"prefix":"10.1007","volume":"1","author":[{"given":"Fangyu","family":"Li","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Junnuo","family":"Lin","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Honggui","family":"Han","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,3,17]]},"reference":[{"key":"6_CR1","doi-asserted-by":"publisher","first-page":"176","DOI":"10.1016\/j.jmsy.2020.11.017","volume":"58","author":"D Pivoto","year":"2021","unstructured":"Pivoto D, Fernandes L, Righi R, Rodrigues J, Lugli A, Alberti A (2021) Cyber-physical systems architectures for industrial internet of things applications in Industry 4.0: a literature review. 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