As a pilot and cybersecurity researcher, I am very interested of the nexus between aviation and security. To explore this interest, I developed a device called Fly Catcher - a device that detects for aircraft spoofing by monitoring for malicious ADS-B signals in the 1090MHz frequency. The device consists of a 1090 MHz antenna, a Flight Aware RTL SDR, a custom 3D printed case, a portable battery charger, and a MicroUSB cable. The device receives ADS-B information from the antenna and the software-defined radio, which is then passed into a Convolutional Neural Network written with Python to detect whether or not the aircraft is spoofed. I trained the neural network on a dataset of valid ADS-B signals as well as a generated spoofed set of aircraft signals, to teach Fly Catcher how to detect and flag any suspicious ADS-B signals. It does this by checking for discrepancies in the signal's characteristics, such as its location, velocity, and identification.