A new defense framework enhances deep neural networks' resilience to false data injection attacks in power grids by adding a padding layer with pseudofeatures derived from input statistical distributions. This lightweight, model-agnostic approach increases input dimensionality in a randomized, data-aware way, making adversarial perturbations non-transferable and unpredictable, thus effectively countering attacks without performance degradation.