Detecting Ghost Aircraft Flooding in the Surveillance of Low-Flying Civil and Military Aircraft

In the past few years, the increasing number of aircraft in low-altitude traffic has brought new challenges for air traffic controllers. As the ADS-B protocol takes an important part in the air surveillance task, it is important to be prepared to face attacks related to it. Moreover, as the ADS-B is open and uses broadcasts, it is easy for an attacker to emit fake ADS-B data to flood the air traffic controller's screens. In this work, we will present two algorithms, the first one is based on hashing techniques, invariant to geometric transformations to detect trajectory replays. The second one uses a deep-learning model to detect irrelevant ghost trajectories. Our proposed approach can automatically filter a majority of ghost aircraft without risking to filter real aircraft.