This joint R&D project between KETEP, University of Salerno, and Gachon University focuses on developing advanced cybersecurity mechanisms for Smart Grids and cyber-physical systems. The research tackles the challenges of securing real-time distributed resources against sophisticated cyberattacks by integrating machine learning, federated learning, and quantum machine learning approaches.
Key contributions include:
- Intrusion Detection on Constrained Devices: Implementation of lightweight ML-based IDS on ESP32 microcontrollers for IoT-enabled smart grids.
- Federated Learning: Privacy-preserving intrusion detection across distributed devices without sharing raw data.
- Quantum Machine Learning: Use of hybrid quantum-classical models and quantum GANs to enhance anomaly detection and adversarial robustness.
- Authentication in the Quantum Era: Design and evaluation of Quantum Physical Unclonable Functions (Q-PUFs) for device authentication.
- Digital Twin Security: Leveraging digital twins and blockchain to prevent data tampering and enable resilient energy management.
Overall, the project contributes to maximizing the availability and resilience of smart grid infrastructures, ensuring secure and trustworthy operation of critical energy systems in the face of evolving cyber threats
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