AdaptEdge: Targeted Universal Adversarial Attacks on Time Series Data in Smart Grids

Abstract

Deep learning (DL) has emerged as a key technique in smart grid operations for task classification of power quality disturbances (PQDs). Even though these models have considerably improved the efficiency of power infrastructure, their susceptibility to adversarial attacks presents potential difficulties. For the first time, we introduce a novel algorithm called Adaptive Edge (AdaptEdge), which effectively employs targeted universal adversarial attack to deceive DL models working with time series data. The unique contribution of this algorithm is its ability to maintain a delicate balance between the fooling rate and the imperceptibility of perturbations to human observers. Our results demonstrate a fooling rate of up to 90.78% in the ResNet50 model-the highest achieved thus far-while maintaining an optimal signal-to-noise ratio (SNR) of 3dB and ensuring signal integrity. We implemented our algorithm across various advanced DL models and found considerable efficacy, demonstrating its adaptability and versatility across diverse architectures. The results of our study highlight the pressing need for developing more robust DL model implementations in the context of the smart grid. Additionally, our proposed approach demonstrates its effectiveness in addressing this need.