Efficient Multi-Source Self-Attention Data Fusion for FDIA Detection in Smart Grid

Abstract

As a new cyber-attack method in power cyber physical systems, false-data-injection attacks (FDIAs) mainly disturb the operating state of power systems by tampering with the measurement data of sensors, thereby avoiding bad-data detection by the power grid and threatening the security of power systems. However, existing FDIA detection methods usually only focus on the detection feature extraction between false data and normal data, ignoring the feature correlation that easily produces diverse data redundancy, resulting in the significant difficulty of detecting false-data-injection attacks. To address the above problem, we propose a multi-source self-attention data fusion model for designing an efficient FDIA detection method. The proposed data fusing model firstly employs a temporal alignment technique to integrate the collected multi-source sensing data to the identical time dimension. Subsequently, a symmetric hybrid deep network model is built by symmetrically combining long short-term memory (LSTM) and a convolution neural network (CNN), which can effectively extract hybrid features for different multi-source sensing data. Furthermore, we design a self-attention module to further eliminate hybrid feature redundancy and aggregate the differences between attack-data features and normal-data features. Finally, the extracted features and their weights are integrated to implement false-data-injection attack detection using a single convolution operation. Extensive simulations are performed over IEEE14 node test systems and IEEE118 node test systems; the experimental results demonstrate that our model can achieve better data fusion effects and presents a superior detection performance compared with the state-of-the-art.