The rapid modernization of smart renewable energy grids has increased dependency on distributed digital monitoring, edge intelligence, and interconnected communication networks, making grid infrastructures highly vulnerable to sophisticated cyber-attacks. Traditional centralized security mechanisms often suffer from latency, high computational overhead, and delayed response in real-time grid environments. To address these challenges, this paper proposes a Memristor-Based Nanoelectronic Edge Architecture for Smart Renewable Energy Grid Cybersecurity, integrating memristive nanoelectronic circuits with edge-based intelligent threat detection for secure and energy-efficient grid protection. The proposed architecture employs memristor crossbar arrays for ultra-low-power parallel data processing, enabling real-time anomaly detection and attack classification directly at distributed edge nodes within the grid. A hybrid lightweight deep learning classifier is embedded into the memristive hardware framework to identify false data injection, denial-of-service, spoofing, and intrusion-based attacks across renewable energy substations and IoT-enabled smart meters. Furthermore, adaptive threat prioritization and dynamic risk scoring mechanisms are incorporated to enhance decision-making accuracy under dynamic grid conditions. Experimental validation performed on benchmark smart grid cybersecurity datasets demonstrates that the proposed framework achieves 98.72% detection accuracy, 97.94% precision, 98.11% recall, and 98.02% F1-score, outperforming conventional CMOS and cloud-based security architectures. Additionally, the memristor-based implementation reduces computational latency by 34.6%, decreases power consumption by 41.3%, and improves edge inference throughput by 29.8% compared with traditional hardware accelerators. The proposed framework provides a scalable, lowpower, and intelligent cybersecurity solution for next-generation resilient renewable energy infrastructures.
Krishna et al. (2026) studied this question.