A Comparative Study of Deep Learning Architectures for Detection of Anomalous ADS-B Messages*

Since the 1920’s, air traffic is becoming more prevalent by the year which results in a steady increase of the number of aircrafts roaming the airspace. This requires the expansion of the air surveillance systems in order to be able to manage each one of these aircrafts. Such an accommodation is planned to be implemented using different technologies and notably the Automatic Dependent Surveillance Broadcast (ADS-B) system. The ADS-B protocol is based on the idea that aircrafts as well as air traffic controllers communicate with each other using messages. However, for practicality reasons, those messages are not encrypted thus malicious messages can be injected. Hence, these attacks need to be detected to ensure the safety of the protocol. In this paper, we evaluate deep learning architectures for the purpose of detecting anomalous/malicious ADS-B messages, especially LSTM architectures which appear to be the most promising ones.