Light-weight slow-rate attack detection framework for resource-constrained Industrial Cyber–Physical Systems

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%.