Light-weight Slow-rate Attack Detection Framework for Resource-constrained Industrial Cyber–Physical Systems
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Elsevier
Abstract
Industrial 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%.Description
Keywords
46 Information and Computing Sciences, 4604 Cybersecurity and Privacy, 08 Information and Computing Sciences, Strategic, Defence & Security Studies, 4604 Cybersecurity and privacy, Industrial Cyber–Physical System, Resource-constrained, Slow-rate attacks, Online Sequential-Extreme Learning Machine, PLC
Source
Computers and Security, ISSN: 0167-4048 (Print); 1872-6208 (Online), Elsevier, 156, 104508-104508. doi: 10.1016/j.cose.2025.104508
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© 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/ ).