An LSTM autoencoder model was finalized to identify anomalous conditions within large-scale cyber-physical power grids. The technique reconstructs baseline operational data and flags deviations based on reconstruction error thresholds. Integrated testing with a graph-embedding risk assessment (GEACRA) highlights compromised components with greater precision, supporting more targeted response actions against malicious intrusions or unexpected failures.