Abstract The actuator is the critical component of the unmanned aerial vehicle (UAV). The interference and suppression to the signal of UAV’s actuators are challenging to directly detect or physically mitigate, thus posing a significant threat to UAV flight safety. Under the assumption of sensor integrity, the state-of-the-art physics-based attack detection approaches can identify the actuator attacks at runtime. However, when both sensor and actuator attacks are allowed simultaneously, such physics-based attack detection approaches cannot differentiate between the two physical attacks, thereby failing to locate the specific compromised actuators or maintain the UAV’s resilience to the actuator attack at runtime. This paper presents EADR , an efficient runtime framework for detecting and recovering from UAV actuator attacks. By leveraging the existing signal-characteristic-based sensor attack detection mechanism, EADR prevents potential sensor attacks from impacting the resilience of actuator attacks. In response to typical attack scenarios, we implement actuator attack detection based on the nonlinear dynamic model combined with the cumulative sum (CUSUM) detection algorithm. We further locate the specific compromised actuators, determine the required compensations for the signal of these actuators at runtime, and apply the compensations to the actuators to effectively recover the UAV system state. The experimental results demonstrate that the time to detection (TTD) of EADR ’s detector is significantly reduced compared with the state-of-the-art approaches. EADR ’s recovery mechanism can reduce the flight positional error by approximately 56.3 – 77.6%. The average runtime overhead of EADR is less than 2%, ensuring the real-time performance required for real-world UAV flight.
Sun et al. (Mon,) studied this question.