# Infrastructure-enabled GPS Spoofing Detection and Correction

arXiv: https://arxiv.org/abs/2202.05463 · HTML: https://mlsanity.com/paper/2202.05463/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Feb 2022.

## Built on by (most cited first, 3 of 3)

- [Data Poisoning Attacks in Intelligent Transportation Systems: A Survey](https://mlsanity.com/paper/2407.15855/) (2024)
- [High-Order Liquid Evidence Encoding for Gradual GNSS Spoofing Detection in Autonomous Driving](https://mlsanity.com/paper/2608.11790/) (2026)
- [High-Order Liquid Evidence Modeling for Continuous and Subtle GNSS Spoofing Detection in Autonomous Driving](https://mlsanity.com/paper/2609.26231/) (2026)

## Abstract (arXiv)

Accurate and robust localization is crucial for supporting high-level driving automation and safety. Modern localization solutions rely on various sensors, among which GPS has been and will continue to be essential. However, GPS can be vulnerable to malicious attacks and GPS spoofing has been identified as a high threat. With transportation infrastructure becoming increasingly important in supporting emerging vehicle technologies and systems, this study explores the potential of applying infrastructure data for defending against GPS spoofing. We propose an infrastructure-enabled framework using roadside units as an independent, secured data source. A real-time detector, based on the Isolation Forest, is constructed to detect GPS spoofing. Once spoofing is detected, GPS measurements are isolated, and the potentially compromised location estimator is corrected using secure infrastructure data. We test the proposed method using both simulation and real-world data and show its effectiveness in defending against various GPS spoofing attacks, including stealthy attacks that are proposed to fail the production-grade autonomous driving systems.