Hybrid Cyber Defense Mechanism with PINN for Resilient and Reliable Control against Replay / FDI Attacks in DC Microgrid Systems

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...

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Vydáno v:IEEE transactions on industry applications s. 1 - 12
Hlavní autoři: Machina, Venkata Siva Prasad, Koduru, Sriranga Suprabhath, Madichetty, Sreedhar, Mishra, Sukumar
Médium: Journal Article
Jazyk:angličtina
Vydáno: IEEE 2025
Témata:
Adaptation models
and Replay attack
Autoencoders
Bidirectional LSTM autoencoder
Computer crime
Cross-correlation
Kalman filter
Kalman filters
Observers
Physics-informed neural networks
Prevention and mitigation
Real-time systems
Regulators
Resilience
Voltage control
ISSN:0093-9994, 1939-9367
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Abstract 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 <inline-formula><tex-math notation="LaTeX">KB</tex-math></inline-formula> of memory with an execution time of 0.4 <inline-formula><tex-math notation="LaTeX">ms</tex-math></inline-formula>. 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.
AbstractList 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 <inline-formula><tex-math notation="LaTeX">KB</tex-math></inline-formula> of memory with an execution time of 0.4 <inline-formula><tex-math notation="LaTeX">ms</tex-math></inline-formula>. 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.
Author Koduru, Sriranga Suprabhath
Machina, Venkata Siva Prasad
Mishra, Sukumar
Madichetty, Sreedhar
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Snippet With the growing reliance on DC microgrids (DC MGs) in critical infrastructure, securing them against sophisticated cyberattacks is essential. This study...
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SubjectTerms Adaptation models
and Replay attack
Autoencoders
Bidirectional LSTM autoencoder
Computer crime
Cross-correlation
Kalman filter
Kalman filters
Observers
Physics-informed neural networks
Prevention and mitigation
Real-time systems
Regulators
Resilience
Voltage control
Title Hybrid Cyber Defense Mechanism with PINN for Resilient and Reliable Control against Replay / FDI Attacks in DC Microgrid Systems
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