GPS Spoofing Mitigation and Timing Risk Analysis in Networked PMUs via Stochastic Reachability

TL;DR

This paper introduces a stochastic reachability-based distributed Kalman filter to mitigate GPS spoofing in networked PMUs, enhancing timing accuracy and providing risk analysis against standards violations.

Contribution

The paper presents a novel SR-DKF method that combines stochastic reachability and probabilistic zonotopes for secure GPS timing and spoofing risk assessment.

Findings

Improved GPS timing accuracy under spoofing conditions.

Effective spoofing mitigation demonstrated in simulated network.

Robustness of timing risk estimates across different receiver counts.

Abstract

To address PMU vulnerability against spoofing, we propose a set-valued state estimation technique known as Stochastic Reachability-based Distributed Kalman Filter (SR-DKF) that computes secure GPS timing across a network of receivers. Utilizing stochastic reachability, we estimate not only GPS time but also its stochastic reachable set, which is parameterized via probabilistic zonotope (p-Zonotope). While requiring known measurement error bounds in only non-spoofed conditions, we design a two-tier approach: We first perform measurement-level spoofing mitigation via deviation of measurement innovation from its expected p-Zonotope and second perform state-level timing risk analysis via intersection probability of estimated pZonotope with an unsafe set that violates IEEE C37.118.1a-2014 standards. We validate the proposed SR-DKF by subjecting a simulated receiver network to coordinated signal-level spoofing. We demonstrate improved GPS timing accuracy and successful spoofing mitigation via our SR-DKF. We validate the robustness of the estimated timing risk as the number of receivers is varied.