# A Reinforcement Learning Approach for GNSS Spoofing Attack Detection of Autonomous Vehicles

arXiv: https://arxiv.org/abs/2108.08628 · HTML: https://mlsanity.com/paper/2108.08628/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Placed in Adversarial scenarios and safety testing, one of 9 directions in End-to-end autonomous driving.
- Builds on 5 of its 45 references that are in the map.
- First posted on arXiv 19 Aug 2021.

## Placed in

- End-to-end autonomous driving / [Adversarial scenarios and safety testing](https://mlsanity.com/driving/adversarial-scenarios-and-safety-testing/)

## Builds on (most cited first, 5 of 5)

- [Toward Driving Scene Understanding: A Dataset for Learning Driver Behavior and Causal Reasoning](https://mlsanity.com/paper/1811.02307/) (2018)
- [Deep Reinforcement Learning for Intelligent Transportation Systems: A Survey](https://mlsanity.com/paper/2005.00935/) (2020)
- [Prediction-Based GNSS Spoofing Attack Detection for Autonomous Vehicles](https://mlsanity.com/paper/2010.11722/) (2020)
- [LEAP: A Lightweight Encryption and Authentication Protocol for In-Vehicle Communications](https://mlsanity.com/paper/1909.10380/) (2019)
- [Security in Automotive Networks: Lightweight Authentication and Authorization](https://mlsanity.com/paper/1703.03652/) (2017)

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

- [Benchmarking Deep Reinforcement Learning for Navigation in Denied Sensor Environments](https://mlsanity.com/paper/2410.14616/) (2024)
- [Audio Analytics-based Human Trafficking Detection Framework for Autonomous Vehicles](https://mlsanity.com/paper/2209.04071/) (2022)
- [High-Order Liquid Evidence Encoding for Gradual GNSS Spoofing Detection in Autonomous Driving](https://mlsanity.com/paper/2608.11790/) (2026)

## Abstract (arXiv)

A resilient and robust positioning, navigation, and timing (PNT) system is a necessity for the navigation of autonomous vehicles (AVs). Global Navigation Satelite System (GNSS) provides satellite-based PNT services. However, a spoofer can temper an authentic GNSS signal and could transmit wrong position information to an AV. Therefore, a GNSS must have the capability of real-time detection and feedback-correction of spoofing attacks related to PNT receivers, whereby it will help the end-user (autonomous vehicle in this case) to navigate safely if it falls into any compromises. This paper aims to develop a deep reinforcement learning (RL)-based turn-by-turn spoofing attack detection using low-cost in-vehicle sensor data. We have utilized Honda Driving Dataset to create attack and non-attack datasets, develop a deep RL model, and evaluate the performance of the RL-based attack detection model. We find that the accuracy of the RL model ranges from 99.99% to 100%, and the recall value is 100%. However, the precision ranges from 93.44% to 100%, and the f1 score ranges from 96.61% to 100%. Overall, the analyses reveal that the RL model is effective in turn-by-turn spoofing attack detection.