Cyber-Physical Systems for Enhancing Security in Critical Infrastructure: Addressing Nigeria’s Security Challenges

Abstract

Cyber-Physical Systems (CPS) are increasingly central to the operation of critical, particularly in emerging economies where digital transformation is accelerating amid limited cybersecurity maturity. This study proposes and validates a context-specific, multi layered CPS security and resilience framework tailored to Nigeria’s infrastructural realities. The framework integrates architectural segmentation (based on the Purdue Model), AI-driven anomaly detection using Long Short-Term Memory (LSTM) networks, and resilience engineering practices such as automated failover and safe-state recovery. Using a simulation testbed emulating smart grid and campus energy systems, the framework was benchmarked against traditional and AI-only detection baselines. Results show that the hybrid architecture improves detection accuracy to 97.8%, reduces false positives to under 1%, and decreases Mean Time to Recovery (MTTR) from 7.8 to 1.9 hours. Downtime was reduced by 88.9%, and cost-benefit analysis revealed a 133% return on investment (ROI) within the first year. These gains were validated through historical event logs and economic modeling using avoided-loss estimation. This framework offers a deployable roadmap for enhancing national cyber resilience in resource constrained settings by combining advanced control algorithms, layered defense principles, and contextual policy alignment. It is particularly relevant to sectors like education, energy, and industrial automation, where CPS integration is outpacing security governance. The findings emphasize that a tailored, resilience-by-design approach rooted in real-time analytics and system containment can substantially improve operational continuity and economic sustainability for critical infrastructure operators.