Learning-based Detection of GPS Spoofing Attack for Quadrotors

TL;DR

This paper introduces QUADFormer, a transformer-based framework that enhances GPS spoofing attack detection in quadrotor UAVs by effectively handling nonlinear dynamics and non-Gaussian noise, improving safety and detection accuracy.

Contribution

The paper presents a novel transformer-based detection framework, QUADFormer, specifically designed for UAVs, addressing limitations of traditional methods in complex outdoor environments.

Findings

QUADFormer outperforms existing methods in detection accuracy.

The framework effectively handles nonlinear UAV dynamics and non-Gaussian noise.

Simulation and experimental results validate its robustness and effectiveness.

Abstract

Safety-critical cyber-physical systems (CPS), such as quadrotor UAVs, are particularly prone to cyber attacks, which can result in significant consequences if not detected promptly and accurately. During outdoor operations, the nonlinear dynamics of UAV systems, combined with non-Gaussian noise, pose challenges to the effectiveness of conventional statistical and machine learning methods. To overcome these limitations, we present QUADFormer, an advanced attack detection framework for quadrotor UAVs leveraging a transformer-based architecture. This framework features a residue generator that produces sequences sensitive to anomalies, which are then analyzed by the transformer to capture statistical patterns for detection and classification. Furthermore, an alert mechanism ensures UAVs can operate safely even when under attack. Extensive simulations and experimental evaluations highlight that QUADFormer outperforms existing state-of-the-art techniques in detection accuracy.