Intelligent GPS Spoofing Attack Detection in Power Grids

TL;DR

This paper proposes a neural network-based method to detect GPS spoofing attacks in power grid PMUs, ensuring accurate time and phase measurements crucial for grid stability.

Contribution

It introduces a novel neural network detection approach utilizing PMU data for real-time GPS spoofing attack detection in power systems.

Findings

Effective real-time detection demonstrated under various conditions.

Neural network approach outperforms traditional detection methods.

Enhances security and reliability of power grid measurements.

Abstract

The GPS is vulnerable to GPS spoofing attack (GSA), which leads to disorder in time and position results of the GPS receiver. In power grids, phasor measurement units (PMUs) use GPS to build time-tagged measurements, so they are susceptible to this attack. As a result of this attack, sampling time and phase angle of the PMU measurements change. In this paper, a neural network GPS spoofing detection (NNGSD) with employing PMU data from the dynamic power system is presented to detect GSAs. Numerical results in different conditions show the real-time performance of the proposed detection method.