# GPS Spoofing Attack Detection in Autonomous Vehicles Using Adaptive DBSCAN

arXiv: https://arxiv.org/abs/2510.10766 · HTML: https://mlsanity.com/paper/2510.10766/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 40 references that are in the map.
- First posted on arXiv 12 Oct 2025.

## 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 Learning-Based Autonomous Driving Systems: A Survey of Attacks and Defenses](https://mlsanity.com/paper/2104.01789/) (2021)
- [Sensor Fusion-based GNSS Spoofing Attack Detection Framework for Autonomous Vehicles](https://mlsanity.com/paper/2106.02982/) (2021)
- [Sanitizing Manufacturing Dataset Labels Using Vision-Language Models](https://mlsanity.com/paper/2506.23465/) (2025)
- [MATTER: Multi-stage Adaptive Thermal Trojan for Efficiency & Resilience degradation](https://mlsanity.com/paper/2412.00226/) (2024)

## Built on by (most cited first, 2 of 2)

- [An Experimental Study of Trojan Vulnerabilities in UAV Autonomous Landing](https://mlsanity.com/paper/2510.20932/) (2025)
- [Volatility in Certainty (VC): A Metric for Detecting Adversarial Perturbations During Inference in Neural Network Classifiers](https://mlsanity.com/paper/2511.11834/) (2025)

## Abstract (arXiv)

As autonomous vehicles become an essential component of modern transportation, they are increasingly vulnerable to threats such as GPS spoofing attacks. This study presents an adaptive detection approach utilizing a dynamically tuned Density Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm, designed to adjust the detection threshold (ε) in real-time. The threshold is updated based on the recursive mean and standard deviation of displacement errors between GPS and in-vehicle sensors data, but only at instances classified as non-anomalous. Furthermore, an initial threshold, determined from 120,000 clean data samples, ensures the capability to identify even subtle and gradual GPS spoofing attempts from the beginning. To assess the performance of the proposed method, five different subsets from the real-world Honda Research Institute Driving Dataset (HDD) are selected to simulate both large and small magnitude GPS spoofing attacks. The modified algorithm effectively identifies turn-by-turn, stop, overshoot, and multiple small biased spoofing attacks, achieving detection accuracies of 98.621%, 99.960.1%, 99.880.1%, and 98.380.1%, respectively. This work provides a substantial advancement in enhancing the security and safety of AVs against GPS spoofing threats.