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This study introduces a lightweight, blockchain-enabled federated intrusion prevention platform. Our research integrates three fundamental concepts: (i) a Proof-of-Trust (PoT) agreement mechanism specifically designed for IIoT devices with limited resources, replacing traditional consensus algorithms that use a lot of processing power; (ii) Byzantine fault-tolerant trust management that can keep the system\u2019s integrity even with up to 33% malicious participants; and (iii) an integrated communication optimization strategy that combines model quantization and gradient sparsification. The system helps find strange behavior at the edge, keeps trust via blockchain, and trains models with federated learning while keeping privacy. Extensive experiments on four benchmark datasets (ToN-IoT, UNSW-NB15, CIC-IDS-2017, and a custom IIoT dataset with 1.25 million records) show that this approach works better than others: it has a 97.8% detection accuracy, a 1.2% false positive rate, an average response time of 85\u2009ms, and an 80% reduction in communication overhead. This work presents the inaugural architecture for the integration of blockchain-based trust management and federated learning, specifically tailored for resource-constrained IIoT contexts, hence introducing a novel paradigm for integrated cybersecurity while preserving data sovereignty.<\/jats:p>","DOI":"10.1186\/s13677-026-00843-3","type":"journal-article","created":{"date-parts":[[2026,1,29]],"date-time":"2026-01-29T10:47:29Z","timestamp":1769683649000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["A lightweight blockchain-enabled federated intrusion prevention framework for resource-constrained industrial IoT devices to detect and mitigate emerging cyberattacks"],"prefix":"10.1186","volume":"15","author":[{"given":"Dinesh Kumar","family":"Nishad","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rashmi","family":"Singh","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Saifullah","family":"Khalid","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,1,29]]},"reference":[{"key":"843_CR1","doi-asserted-by":"publisher","first-page":"410","DOI":"10.1016\/j.procs.2024.05.048","volume":"236","author":"K Shalabi","year":"2024","unstructured":"Shalabi K, Al-Haija QA, Al-Fayoumi M (2024) A blockchain-based intrusion Detection\/Prevention systems in IoT network: a systematic review. 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All experimental procedures complied with institutional ethics guidelines. No human subjects were directly involved. Data handling followed established ethical standards for network security research.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable as the manuscript does not contain data from any person.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"24"}}