GPS Spoofing Attack Detection in Autonomous Vehicles Using Adaptive DBSCAN

2025-10-12

AI Summary

Autonomous vehicles are vulnerable to GPS spoofing attacks—particularly subtle, incremental deviations—that compromise localization integrity. To address this, we propose a real-time detection method based on adaptive DBSCAN. Our approach recursively updates the mean and standard deviation of displacement errors under normal operation to dynamically optimize the DBSCAN neighborhood radius ε. By fusing displacement errors from GPS, IMU, and wheel-speed sensors, we construct a density-based anomaly detection model; clean-data-driven threshold initialization further enhances early attack identification. Evaluated on the Honda driving dataset, the method achieves detection accuracies of 98.62%, 99.96%, 99.88%, and 98.38% for steering, emergency braking, overshoot, and multi-segment small-bias attacks, respectively. This significantly improves localization security and robustness against sophisticated GPS spoofing.

Technology Category

• Intelligent Robots: Localization, Mapping, and Navigation
• Computer Vision: Adversarial Attacks & Robustness
• Data Mining & Knowledge Management: Anomaly/Outlier Detection

Application Category

• Security and Privacy: Large-scale security measurements
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Systems and Infrastructure for Web, Mobile and WoT: Location- and context-aware Web and WoT applications and services

Abstract

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.

Problem

• Detects GPS spoofing attacks in autonomous vehicles
• Uses adaptive DBSCAN with real-time threshold adjustment
• Identifies various spoofing types using sensor displacement errors

Innovation

• Adaptive DBSCAN algorithm with real-time threshold tuning
• Dynamic threshold update using displacement error statistics
• Initial threshold from clean data detects subtle spoofing