I Know Where You Are Going: Predicting Flight Destinations of Corporate and State Aircraft
Abstract
As data of aircraft movements have become freely accessible on a large scale through means of crowdsourcing, their open source intelligence (OSINT) value has been illustrated in many different domains. Potentially sensitive movements of all stakeholders outside commercial aviation are potentially affected, from corporate jets to military and government aircraft. Until now, this OSINT value was shown only on historical data, where automated analysis on flight destinations has been effective to find information on potential mergers & acquisition deals or diplomatic relationships between governments. In practice, obtaining such information as early as possible is crucial. Hence, in this work, we predict the destinations of state and corporate aircraft on live data, while the targets are still in the air. We use machine learning algorithms to predict the area of landing up to 2 h in advance. We evaluate our approach on more than 500,000 flights during 2018 obtained from the OpenSky Network.
Marc Jourdan
PhD Student
PhD student at Scool (Inria), I study identification problems in Multi-Armed Bandits.
Karolis Martinkus
PhD Student
