GPS Spoofing attack detection in smart grids based on improved CapsNet

Abstract

This paper analyzes the influence of the global positionong system (GPS) spoofing attack (GSA) on phasor measurement units (PMU) measurements. We propose a detection method based on improved Capsule Neural Network (CapsNet) to handle this attack. In the improved CapsNet, the gated recurrent unit (GRU) is added to the front of the full connection layer of the CapsNet. The improved CapsNet trains and updates the network parameters according to the historical measurements of the smart grid. The detection method uses different structures to extract the temporal and spatial features of the measurements simultaneously, which can accurately distinguish the attacked data from the normal data, to improve the detection accuracy. Finally, simulation experiments are carried out on IEEE 14-, IEEE 118-bus systems. The experimental results show that compared with other detection methods, our method is proved to be more efficient.