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) …

With the increase in the 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 postincident 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 work, we propose novel architectures based on deep convolutional and
long short-term memory neural networks to detect (via autoencoder) and classify drone …