Towards reliable intrusion detection in smart grids: A multiclass, process-aware perspective
A systematic evaluation of the Sherlock dataset proves that, when high-fidelity event labels are available, supervised multi-class learning provides a robust, accurate, and engineering-valid foundation for intrusion detection in the modern, renewable-integrated grids.