  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.
