F. Albeladi

A statistically defensible machine learning pipeline for FDIA detection and resilience evaluation in renewable smart grids

False data injection attacks represent a serious cyber-physical security challenge for modern smart grids because falsified measurements can affect state estimation, energy management, and operational decision-making. While various machine learning-based FDIA detection methods have been investigated in literature, ma...