Fly Catcher monitors for malicious ADS-B signals in the 1090MHz frequency to detect for aircraft spoofing

Learn More | Build Guide | Getting Started | Video | Research Paper | Article

• 🔎 Detecting spoofed ADS-B messages
• 📡 Logging messages on the 1090 MHz frequency
• ✈️ Mapping and visualizing ADS-B messages
• ⚙️ A portable Raspberry-Pi based device
• ⚡️ An accurate neural network classifier
• 🔨 3D printable case with small form factor
• 📻 Compatible with the FlightAware SDR

Picture of the completed build

Device shown with the SportCruiser

Display shown on the TFT Screen

Watch the video overview of Fly Catcher on YouTube

https://youtube.com/watch?v=NJ9ep0IlddA

• 1090MHz Rubber Ducky Antenna
• Raspberry Pi 3B
• FlightAware Pro Stick Plus SDR
• 3.5 in TFT Screen
• Portable Battery Charger
• USB-C to Micro USB Cable
• Custom 3D Printed Case
• SD Card
• Rasbian Operating System
• 4x 3/32 Screws
• Python and Pip on Raspberry Pi

1. Install the Rasbian operating system to the Raspberry Pi with the SD Card
2. Connect the Flight Aware SDR to the Raspberry Pi using the Micro USB cable
3. Connect the 1090 MHz antenna to the Flight Aware SDR
4. Configure the 3.5-inch TFT Screen to the Raspberry Pi
5. Place the Device into the 3D Printed Case
6. Ensure Python and Pip are installed on the Raspberry Pi
7. Install dump-1090 FlightAware library on the Raspberry Pi to receive ADS-B information

https://www.stuffaboutcode.com/2015/11/raspberry-pi-piaware-aircraft-radar.html

Clone the Repository on the Pi

git clone https://github.com/ANG13T/fly-catcher.git

Run the Program

python3 fly-catcher/device-rpi/piawareradar.py longitude latitude

Replace longitude and latitude with your geo-coordinates

git clone https://github.com/ANG13T/fly-catcher.git
cd notebook
jupyter notebook

Install Jupyter Notebook if you do not have it

Visit the IP address of the Raspberry Pi device followed by the path /data/aircraft.json For example, 192.168.1.114:8080/data/aircraft.json

To get a more in-depth and technical overview of Fly Catcher, you can refer to this research paper.

You can also read an article write-up I made about Fly Catcher here.

• Enhanced UI features on the radar screen
• Deep learning techniques such as RNNs and LSTM networks
• Incorporating reinforcement learning techniques
• Differentiate spoofing attacks (ie. GPS spoofing, aircraft masquerading, etc)

Fly Catcher is open to any contributions. Please fork the repository and make a pull request with the features or fixes you want to implement.

The Fly Catcher leveraged on previous ADS-B works and references included below

• Pi Aware Radar by Martin O'Hanlon
• Reference dump1090 README
• Data Samples from ADSB Exchange
• IEEE Research on ADS-B Signals

If you enjoyed Fly Catcher, please consider becoming a sponsor in order to fund my future projects.

To check out my other works, visit my GitHub profile.