Using Autoencoders To Learn Interesting Features For Detecting Surveillance Aircraft

Abstract--This paper explores using a Long short-term memory (LSTM) based sequence autoencoder to learn interesting features for detecting surveillance aircraft using ADS-B flight data. An aircraft periodically broadcasts ADS-B (Automatic Dependent Surveillance - Broadcast) data to ground receivers. The ability of LSTM networks to model varying length time series data and remember dependencies that span across events makes it an ideal candidate for implementing a sequence autoencoder for ADS-B data because of its possible variable length time series, irregular sampling and dependencies that span across events. The motivation for this research was inspired by the original research presented by Richards, MacDonald-Evoy, and Hernandez in their "Tracking Spies In The Skies" talk at DEF CON 25 [1]. The goal of their research is to leverage ADS-B (Automatic Dependent Surveillance - Broadcast) data that is broadcast by commercial and private aircraft to detect surveillance aircraft.