Tuning-Free, Low Memory Robust Estimator to Mitigate GPS Spoofing Attacks

TL;DR

This work tackles the vulnerability of stationary GPS timing to Time Synchronization Attacks by proposing a tuning-free, low-memory robust estimator built on an observer design for linear systems with unknown inputs. The method introduces a modified state, offline LMIs to compute gains, and a real-time state correction mechanism that reconstructs authentic clock bias $b_u$ and drift $\dot b_u$ despite spoofing disturbances $\bm d[k]$. It demonstrates strong empirical performance on real GPS data with Type I and II TSAs, outperforming EKF and Luenberger observers in RMSE and convergence speed, and it provides a practical, reproducible workflow with a realistic testbed. The approach holds promise for protecting timing-critical infrastructures by delivering accurate time under spoofing while requiring minimal tuning and computational resources, with future work targeting non-stationary and nonlinear GPS measurement models.

Abstract

The operation of critical infrastructures such as the electrical power grid, cellphone towers, and financial institutions relies on precise timing provided by stationary GPS receivers. These GPS devices are vulnerable to a type of spoofing called Time Synchronization Attack (TSA), whose objective is to maliciously alter the timing provided by the GPS receiver. The objective of this paper is to design a tuning-free, low memory robust estimator to mitigate such spoofing attacks. The contribution is that the proposed method dispenses with several limitations found in the existing state-of-the-art methods in the literature that require parameter tuning, availability of the statistical distributions of noise, real-time optimization, or heavy computations. Specifically, we (i) utilize an observer design for linear systems under unknown inputs, (ii) adjust it to include a state-correction algorithm, (iii) design a realistic experimental setup with real GPS data and sensible spoofing attacks, and (iv) showcase how the proposed tuning-free, low memory robust estimator can combat TSAs. Numerical tests with real GPS data demonstrate that accurate time can be provided to the user under various attack conditions.

Figures (5)

• Figure 1: Comparison of ground truth, EKF and Luenberger observer estimates under Type I attack: (a) clock bias; (b) clock drift.
• Figure 2: Comparison of ground truth, EKF and Luenberger observer estimates under Type II attack: (a) clock bias; (b) clock drift.
• Figure 3: Comparison of ground truth and corrected state through the correction \ref{['correction']} under Type I attack: (a) clock bias; (b) clock drift.
• Figure 4: Comparison of ground truth and corrected state through the correction \ref{['correction']} under Type II attack: (a) clock bias; (b) clock drift.
• Figure 5: Relative estimation error for clock bias and drift defined as: $\mathrm{RE}_1 = \frac{\mid\hat{\bm {x}}_{\mathrm{c}}(1)-\bm{x}_{\mathrm{GT}}(1)\mid}{\bm {x}_{\mathrm{GT}}(1)},$$\mathrm{RE}_2 = \frac{\mid\hat{\bm {x}}_{\mathrm{c}}(2)-\bm{x}_{\mathrm{GT}}(2)\mid}{\bm {x}_{\mathrm{GT}}(2)}$.