用Transformer检测无人机GPS欺骗攻击，准确率超现有方法
Learning-based Detection of GPS Spoofing Attack for Quadrotors
- 基于Transformer设计异常序列生成与分析框架
- 在仿真和实验中检测准确率显著优于现有技术
- 适合对无人机安全有高要求的工程应用
安全关键型网络物理系统（如四旋翼无人机）极易遭受网络攻击，若未能及时准确检测，可能导致严重后果。在室外作业中，无人机系统的非线性动力学与非高斯噪声给传统统计与机器学习方法带来挑战。为此，本文提出QUADFormer，一种基于Transformer架构的先进攻击检测框架。该框架包含一个残差生成器，可生成对异常敏感的序列，再由Transformer捕捉统计模式以实现检测与分类。此外，还设计了告警机制，确保无人机在受攻击时仍能安全运行。大量仿真与实验评估表明，QUADFormer在检测准确率上优于现有最先进方法。
原文摘要 · Abstract (English)
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.
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