Blind false data injection attacks in smart grids subject to measurement outliers

Abstract

ABSTRACT False data injection attacks (FDIAs) can manipulate measurement data from Supervisory Control and Data Acquisition (SCADA) system and threat state estimation in smart grids. Blind FDIAs (BFDIAs) enhance traditional FDIAs, which eliminate the limitation of grasping measurement Jacobian matrix H in advance, but when there are outliers in measurement data, attack performance is degraded. In this paper, improved BFDIAs are proposed. In off-line phase, low-dimensional measurement matrix without outliers calculated by Linear Local Tangent Space Alignment algorithm (LLTSA) is sent into Continuous Deep Belief Network (CDBN) as training data to learn their probability distribution. In on-line phase, real-time low-dimensional measurement matrix with outliers are sent into the trained model as inputs, and outputs are reconstructed by the probability distribution in off-line phase, which eliminates the influence of outliers indirectly. Simulations are implemented on PJM 5-bus and IEEE 14-bus systems to verify the performance of proposed strategy compared with PCA-based BFDIAs.