Angelina Tsuboi, a skilled pilot with a cybersecurity background, has introduced an innovative solution named “Fly Catcher,” leveraging the widely-used Raspberry Pi single-board computer. This groundbreaking system is designed to identify counterfeit aircraft signals through the utilization of Automatic Dependent Surveillance Data Relay System (ADS-B) technology.
ADS-B plays a crucial role in aviation by transmitting essential information about an aircraft’s location. However, malicious actors can manipulate this data by simulating the presence of a non-existent aircraft. “Fly Catcher” addresses this security concern by actively monitoring the 1090 MHz frequency and employing a specialized neural network to assess the legitimacy of potential aircraft signals.
The core of the system is a Raspberry Pi, which executes a neural network developed in Python. This algorithm underwent training using accurate ADS-B data to distinguish authentic flight information from fabricated details. Rigorous testing was conducted using fake aircraft data generated by Software-Defined Radio (SDR), mirroring the techniques employed by hackers.
For the implementation, Tsuboi utilized a Raspberry Pi 3 B+ connected to a 1,090 MHz antenna alongside FlightAware SDR equipment to capture real-time ADS-B signals. The system’s performance was put to the test during a one-hour flight over Los Angeles, and the details of this trial are available on Tsuboi’s YouTube channel.
In a commendable move towards fostering collaboration and knowledge sharing within the community, Tsuboi has open-sourced the “Fly Catcher” project. The source code is readily available on GitHub, allowing enthusiasts and developers alike to explore, contribute, and benefit from this security initiative. For a comprehensive introduction to the “Fly Catcher” project, interested individuals can visit the official page on the Hackster website, where detailed information and resources are accessible.
CyberSec84 | Cybersecurity news. 
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