Zahid, FarzanaKuo, Matthew MYSinha, Roopak2025-05-192025-05-192025-05-15Computers and Security, ISSN: 0167-4048 (Print); 1872-6208 (Online), Elsevier, 156, 104508-104508. doi: 10.1016/j.cose.2025.1045080167-40481872-6208http://hdl.handle.net/10292/19228Industrial Cyber–Physical Systems (ICPS) are heterogeneous computer systems interacting with physical processes in an industrial environment. The presence of numerous interconnected components poses significant security threats to ICPS. Slow-Rate Attacks (SRA), in which attackers attack a system constantly at low volumes, are difficult to detect for resource-constrained ICPS computers like programmable logic controllers (PLC). We propose an optimised light-weight active security framework for SRA detection based on Online Sequential Extreme Learning Machine (OSELM). We optimise the memory and space footprint of OSELM for deployment in resource-constrained ICPS. Additionally, a simple stratified k-fold cross training method improves the performance and accuracy of binary and multi-class SRA detection. Compared to existing methods, our technique requires less space and reduces attack detection time by at least 95%.© 2025 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license ( http://creativecommons.org/licenses/by/4.0/ ).https://creativecommons.org/licenses/by/4.0/46 Information and Computing Sciences4604 Cybersecurity and Privacy08 Information and Computing SciencesStrategic, Defence & Security Studies4604 Cybersecurity and privacyIndustrial Cyber–Physical SystemResource-constrainedSlow-rate attacksOnline Sequential-Extreme Learning MachinePLCJournal ArticleOpenAccess10.1016/j.cose.2025.104508