Fly Catcher is an open-source device that can detect aircraft spoofing by monitoring for malicious ADS-B signals in the 1090MHz frequency.
Angelina Tsuboi, the developer of Fly Catcher, is an enthusiastic pilot, cybersecurity researcher, and tinkerer.
She was driven to embark on a project that merged these three distinct interests to address a significant issue in aviation radar systems.
Intrigued by the ADS-B system, initially used for basic plane spotting and tracking, Tsuboi looked more into its cybersecurity aspects.
Surprisingly, she discovered a lack of security measures in place.
Recognizing the extensive use of ADS-B by pilots and ground control, the developer was motivated to utilize their maker skills to raise awareness about this security gap and to create a solution.
With a spare Raspberry Pi, an SDR, and a 1090 MHz antenna at hand, they began the development of Fly Catcher, a device aimed at addressing these security concerns.
More research had to go into optimizing the model to classify more covert spoofing cases.
After tinkering around with various optimizations, I found that RSSI Fingerprinting, which involves classifying spoofed aircraft by analyzing the signal strength of the aircraft ADS-B Out transmitter, was the most accurate, Tsuboi told Help Net Security.
Considering the long-term prospects of the project, the developer aims to enhance the core functionalities of the device to boost its accuracy in detecting spoofs.
At present, the device's training is based on fabricated data, due to the developer's limited access to actual spoofed signals.
Tsuboi is keen on collaborating with signals intelligence experts in the aviation sector to acquire authentic spoofed ADS-B data from real-world situations.
This would significantly improve the AI model powering the device.
The developer is interested in exploring various AI models and paradigms, including RNNs and LSTMs, to refine the model's accuracy.
Go to GitHub to learn in detail how to build this device yourself.
This Cyber News was published on www.helpnetsecurity.com. Publication date: Wed, 10 Jan 2024 06:43:05 +0000