FlightSense: A Spoofer Detection and Aircraft Identification System using Raw ADS-B Data
Nikita Susan Joseph, Chaity Banerjee, Eduardo Pasiliao, Tathagata Mukherjee
Abstract
We introduce a robust neural network based method for classifying aircraft, using raw I/Q data obtained from the Automatic Dependent Surveillance-Broadcast (ADS-B) data from airplanes. ADS-B has become the de-facto standard for air traffic control and forms the basis of the Next Generation Air Transportation System (NextGen). Although ADS-B is at the core of modern day air traffic control, the standard lacks basic security features such as encryption and authentication. As a result, it is possible to spoof ADS-B data and in the process create unprecedented operational havoc in the skies. In this work we propose FlightSense: a robust adversarial learning based system for filtering out spoofed ADS-B data and subsequent identification of airplanes operating in the airspace from the filtered signal. We use the framework of a generative adversarial network (GAN) for our implementation, which is end-to-end in that it uses the raw I/Q signal data as input and no preprocessing steps are required. We present experiments and results to demonstrate the efficacy of our methods using a real world standardized ADS-B dataset.