Hybrid Cyber Defense Mechanism with PINN for Resilient and Reliable Control against Replay / FDI Attacks in DC Microgrid Systems - Machina, Venkata Siva Prasad; Koduru, Sriranga Suprabhath; Madichetty, Sreedhar; Mishra, Sukumar (IEEE transactions on industry applications, 2025)
With the growing reliance on DC microgrids (DC MGs) in critical infrastructure, securing them against sophisticated cyberattacks is essential. This study presents a hybrid cyber-defense (HCD) framework that detects and mitigates false data injection (FDI) and replay attacks through a combination of bidirectional LSTM (Bi-LSTM) autoencoders for anomaly detection, cross-correlation analysis for replay attack identification, and a physics-informed neural network (PINN) for adaptive control. A Kalman filter-based estimator maintains control stability when sensor measurements are compromised. The framework operates in a fully decentralized manner at the local controller level, enabling fast, low-latency responses. Validation on a 4-bus MATLAB/Simulink model and a 3-bus DC MG experimental testbed demonstrates over 95% detection accuracy and effective mitigation, preserving voltage stability under attack. The proposed control algorithm is deployed in the AT19SAM3X8E microcontroller, acting as the local controller at each node, occupying 40 KB of memory with an execution time of 0.4 ms . This real-time deployment confirms practical applicability for lightweight, standalone operation. These results demonstrate the proposed method's robustness and scalability, advancing intelligent, real-time cyber-resilience for future DC MG.