Abstract: Recently, false data injection attacks (FDIAs) have been identified as a significant category of
cyber-attacks targeting smart grids’ state estimation and monitoring systems. These cyber-attacks aim
to mislead control system operations by compromising the readings of various smart grid meters. The
real-time and precise locational identification of FDIAs is crucial for smart grid security and reliability.
This paper proposes a multivariate-based multi-label locational detection (MMLD) mechanism to
detect the presence and locations of FDIAs in real-time measurements with precise locational detection
accuracy. The proposed architecture is a parallel structure that concatenates Long Short-Term Memory
(LSTM) with Temporal Convolutional Neural Network (TCN). The proposed architecture is trained
using Keras with Tensorflow libraries, and its performance is verified using an IEEE standard bus
system in the MATPOWER package. Extensive testing has shown that the proposed approach
effectively improves the presence-detection accuracy for locating stealthy FDIAs in small and large
systems under various attack conditions. In addition, this work provides a customized loss function
for handling the class imbalance problem. Simulation results reveal that our MMLD technique has a
modest advantage in some aspects. First, our mechanism outperforms benchmark models because
the problem is formulated as a multivariate-based multi-label classification problem. Second, it needs
fewer iterations for training and reaching the optimal model. More specifically, our approach is less
complex and more scalable than benchmark algorithms.