Unveiling the Stealthy Threat: Analyzing Slow Drift GPS Spoofing Attacks for Autonomous Vehicles in Urban Environments and Enabling the Resilience

TL;DR

This paper investigates a stealthy slow drift GPS spoofing attack on autonomous vehicles, demonstrating how such attacks can deceive AVs during turns by gradually altering GPS signals, and proposes methods for detection and mitigation.

Contribution

It introduces a novel covert spoofing attack method that mimics satellite reception patterns and analyzes the correlation between original and spoofed signals for defense.

Findings

High correlation (R^2 0.99-1) between spoofed and original pseudo ranges.

Gradual deviation makes real-time detection difficult.

Effective evaluation framework for spoofing mitigation.

Abstract

Autonomous vehicles (AVs) rely on the Global Positioning System (GPS) or Global Navigation Satellite Systems (GNSS) for precise (Positioning, Navigation, and Timing) PNT solutions. However, the vulnerability of GPS signals to intentional and unintended threats due to their lack of encryption and weak signal strength poses serious risks, thereby reducing the reliability of AVs. GPS spoofing is a complex and damaging attack that deceives AVs by altering GPS receivers to calculate false position and tracking information leading to misdirection. This study explores a stealthy slow drift GPS spoofing attack, replicating the victim AV's satellite reception pattern while changing pseudo ranges to deceive the AV, particularly during turns. The attack is designed to gradually deviate from the correct route, making real-time detection challenging and jeopardizing user safety. We present a system and study methodology for constructing covert spoofing attacks on AVs, investigating the correlation between original and spoofed pseudo ranges to create effective defenses. By closely following the victim vehicle and using the same satellite signals, the attacker executes the attack precisely. Changing the pseudo ranges confuses the AV, leading it to incorrect destinations while remaining oblivious to the manipulation. The gradual deviation from the actual route further conceals the attack, hindering its swift identification. The experiments showcase a robust correlation between the original and spoofed pseudo ranges, with R square values varying between 0.99 and 1. This strong correlation facilitates effective evaluation and mitigation of spoofing signals.