  With the increase in use of Unmanned Aerial Vehicles (UAVs)/drones, it is
important to detect and identify causes of failure in real time for proper
recovery from a potential crash-like scenario or post incident forensics
analysis. The cause of crash could be either a fault in the sensor/actuator
system, a physical damage/attack, or a cyber attack on the drone's software. In
this paper, we propose novel architectures based on deep Convolutional and Long
Short-Term Memory Neural Networks (CNNs and LSTMs) to detect (via Autoencoder)
and classify drone mis-operations based on sensor data. The proposed
architectures are able to learn high-level features automatically from the raw
sensor data and learn the spatial and temporal dynamics in the sensor data. We
validate the proposed deep-learning architectures via simulations and
experiments on a real drone. Empirical results show that our solution is able
to detect with over 90% accuracy and classify various types of drone
mis-operations (with about 99% accuracy (simulation data) and upto 88% accuracy
(experimental data)).
