Synthetic false data injection-inspired perturbation framework for power grid attack detection using graph neural networks and deep learning models
The novelty of this work lies in providing a unified, leakage-free comparative framework spanning graph- and non-graph-based deep learning architectures under identical experimental conditions, which provides a realistic and extensible benchmark for future power-grid cybersecurity research.
Niharika Agrawal, Sheila Mahapatra, Bishwajit Dey
· Discover Computing · 0 citations