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.

翻译：安全攸关的智能信息物理系统，如四旋翼无人机，易受各类网络攻击，若缺乏及时精准的攻击检测可能导致严重后果。当无人机进行大规模户外机动飞行时，其系统呈现包含非高斯噪声的高度非线性动态特性。因此，常用的传统基于统计方法与新兴基于学习的攻击检测方法均难以取得理想效果。针对上述挑战，我们提出QUADFormer——一种基于Transformer架构的新型四旋翼无人机攻击检测框架。该框架包含一个专为生成对异常敏感的残差序列而设计的残差生成器。随后，该序列被输入至具有关联差异性的Transformer结构中，以专门学习其统计特征，从而实现分类与攻击检测。最后，我们设计了一个告警模块，以确保无人机在遭受攻击时仍能安全执行任务。我们进行了大量仿真与真实场景实验，结果表明相较于多种先进方法，本方法取得了更优的检测性能。