How I Developed a Low-Cost Raspberry Pi-Based Device for ADS-B Spoof Detection
Details Fly Catcher, a low-cost Raspberry Pi and RTL-SDR device that uses a convolutional neural network to detect spoofed ADS-B aircraft signals.
Summary
The Threat of ADS-B Aviation Spoofing
Automatic Dependent Surveillance–Broadcast (ADS-B) broadcasts unencrypted aircraft flight parameters over 1090 MHz, leaving civilian aviation vulnerable to RF signal spoofing.
The Fly Catcher Hardware Architecture
Fly Catcher is a portable detection device combining a 1090 MHz antenna, FlightAware RTL-SDR receiver, custom 3D-printed enclosure, and a Raspberry Pi.
Neural Network Spoofing Classifier
A Convolutional Neural Network (CNN) trained on authentic and simulated spoofed ADS-B datasets detects signal anomalies across aircraft velocity, altitude, and location telemetry.
In-Flight Field Testing and Validation
Deploying Fly Catcher during an hour-long research flight validated its real-time radar interface and machine learning classification of nearby aircraft signals.
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