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What is Interpretable FDIA Detection in Smart Grids about?

This paper presents DAMGAT, an interpretable deep learning method for detecting false data injection attacks (FDIAs) in smart grids, addressing the limitations of existing detection techniques that often overlook spatial correlations and lack interpretability. The proposed model employs a dual-attention mechanism within a multi-head graph attention network to enhance detection accuracy and provide clear interpretations of the results. Extensive simulations demonstrate that DAMGAT outperforms state-of-the-ar

Author

gekeyo8849

Language

EN