Memristor-Based Nanoelectronic Edge Architecture for Smart Renewable Energy Grid Cybersecurity

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.