QUADFormer: Learning-based Detection of Cyber Attacks in Quadrotor UAVs

TL;DR

QUADFormer is a novel transformer-based framework for detecting cyber attacks in quadrotor UAVs, addressing nonlinear dynamics and noise challenges to improve detection accuracy in safety-critical scenarios.

Contribution

The paper introduces QUADFormer, a new attack detection framework utilizing transformer architecture specifically designed for nonlinear UAV dynamics and non-Gaussian noise environments.

Findings

Achieves superior detection accuracy compared to state-of-the-art methods.

Effective in both simulations and real-world UAV experiments.

Enhances UAV safety by timely attack alerts.

Abstract

Safety-critical intelligent cyber-physical systems, such as quadrotor unmanned aerial vehicles (UAVs), are vulnerable to different types of cyber attacks, and the absence of timely and accurate attack detection can lead to severe consequences. When UAVs are engaged in large outdoor maneuvering flights, their system constitutes highly nonlinear dynamics that include non-Gaussian noises. Therefore, the commonly employed traditional statistics-based and emerging learning-based attack detection methods do not yield satisfactory results. In response to the above challenges, we propose QUADFormer, a novel Quadrotor UAV Attack Detection framework with transFormer-based architecture. This framework includes a residue generator designed to generate a residue sequence sensitive to anomalies. Subsequently, this sequence is fed into a transformer structure with disparity in correlation to specifically learn its statistical characteristics for the purpose of classification and attack detection. Finally, we design an alert module to ensure the safe execution of tasks by UAVs under attack conditions. We conduct extensive simulations and real-world experiments, and the results show that our method has achieved superior detection performance compared with many state-of-the-art methods.