Historical Data Analysis and Modelling for Drone Intrusions in Airports

Airport operations are particularly susceptible to unauthorized drone intrusions and an increasing of the awareness is required in regard to this phenomenon. This work describes a quantitative assessment of the historical features of drone intrusions in airports, by using different public databases with reports about real sightings. The available features are modelled in terms of probability distributions. Also, a risk classification model is proposed by means of supervised machine learning. Lastly, a preliminary analysis is provided for the definition of an Airport Vulnerability Index with respect to drone intrusions.