  Along with the importance of safety, an IDS has become a significant task in
the real world. Prior studies proposed various intrusion detection models for
the UAV. Past rule-based approaches provided a concrete baseline IDS model, and
the machine learning-based method achieved a precise intrusion detection
performance on the UAV with supervised learning models. However, previous
methods have room for improvement to be implemented in the real world. Prior
methods required a large labeling effort on the dataset, and the model could
not identify attacks that were not trained before. To jump over these hurdles,
we propose an IDS with unsupervised learning. As unsupervised learning does not
require labeling, our model let the practitioner not to label every type of
attack from the flight data. Moreover, the model can identify an abnormal
status of the UAV regardless of the type of attack. We trained an autoencoder
with the benign flight data only and checked the model provides a different
reconstruction loss at the benign flight and the flight under attack. We
discovered that the model produces much higher reconstruction loss with the
flight under attack than the benign flight; thus, this reconstruction loss can
be utilized to recognize an intrusion to the UAV. With consideration of the
computation overhead and the detection performance in the wild, we expect our
model can be a concrete and practical baseline IDS on the UAV.
