QUADFormer: クアッドロータUAVにおけるサイバー攻撃の学習ベース検出
QUADFormer: Learning-based Detection of Cyber Attacks in Quadrotor UAVs
トランスフォーマーを用いてクアッドロータUAVへのサイバー攻撃を検出するフレームワークを提案し、実世界実験で従来手法を上回る検出性能を示した。
著者: Pengyu Wang, Zhaohua Yang, Nachuan Yang, Zikai Wang, Jialu Li, Fan Zhang, Chaoqun Wang, Jiankun Wang, Max Q. -H. Meng, Ling Shi
分類: cs.RO
原文アブストラクト
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.
