  Smart grid's objective is to enable electricity and information to flow
two-way while providing effective, robust, computerized, and decentralized
energy delivery. This necessitates the use of state estimation-based techniques
and real-time analysis to ensure that effective controls are deployed properly.
However, the reliance on communication technologies makes such systems
susceptible to sophisticated data integrity attacks imposing serious threats to
the overall reliability of smart grid. To detect such attacks, advanced and
efficient anomaly detection solutions are needed. In this paper, a two-stage
deep learning-based framework is carefully designed by embedding power system's
characteristics enabling precise attack detection and localization. First, we
encode temporal correlations of the multivariate power system time-series
measurements as 2D images using image-based representation approaches such as
Gramian Angular Field (GAF) and Recurrence Plot (RP) to obtain the latent data
characteristics. These images are then utilized to build a highly reliable and
resilient deep Convolutional Neural Network (CNN)-based multi-label classifier
capable of learning both low and high level characteristics in the images to
detect and discover the exact attack locations without leveraging any prior
statistical assumptions. The proposed method is evaluated on the IEEE 57-bus
system using real-world load data. Also, a comparative study is carried out.
Numerical results indicate that the proposed multi-class cyber-intrusion
detection framework outperforms the current conventional and deep
learning-based attack detection methods.
