Detection and localization of false data injection attacks based on multi-scale feature fusion and attention enhancement network in smart grid

Abstract

This study proposes a novel framework based on the multi-scale feature fusion and attention enhancement network (MSFF-AEN) for detecting and localizing false data injection attacks (FDIAs) in smart grid. The model innovatively designs improved residual block with convolutional block attention module (CBAM) after the second convolutional layer, reducing early noise interference, and enhancing interpretability. It also incorporates a bidirectional long short-term memory network (BiLSTM) and multi-head attention (MHA) to capture temporal features and global dependencies respectively. Additionally, hierarchical feature fusion (HFF) with learnable weights optimizes and integrates multi-scale features, thereby enhancing feature representation and model interpretability. Experimental results on the IEEE 14-bus and IEEE 118-bus systems show that the proposed model outperforms existing conventional models and deep learning methods across multiple evaluation metrics, including accuracy, precision, recall, and F1-score. Particularly, the model performs exceptionally well on the large-scale IEEE 118-bus power system, achieving an accuracy of 98.73%, precision of 98.48%, recall of 97.45%, and F1-score of 97.95%. Furthermore, the model demonstrates strong robustness to various Gaussian noise conditions, maintaining high localization accuracy.