Speaker
Description
With the increasing use of unmanned aerial vehicles (UAVs) such as drones, ensuring the security and integrity of such systems has become increasingly important. Current regulations require drones to broadcast Remote ID messages containing information such as the drone's location, velocity, and control station position, intended to be used for identification and situational awareness. However, because Remote ID messages are unencrypted RF broadcasts, they may be vulnerable to spoofing or falsified-data injection attacks.
In this work, we investigate methods for detecting Remote ID spoofing. Preliminary simulation results indicate that a UAV transmitting falsified location information can be detected by comparing received signal strength measurements with the location reported in its Remote ID broadcast. By further extracting RF characteristics from a drone's transmitted signal using GNU Radio, we aim to identify discrepancies between the broadcasted and actual UAV locations. Techniques such as received signal strength indicator (RSSI), Doppler shift estimation, and angle of arrival (AoA) analysis are explored as means of verifying the authenticity of the broadcasted message.
To evaluate these techniques, SDR-based drones systems are implemented for real-time capture and processing of drones Remote ID transmissions. By combining multiple RF-based measurement techniques, the system aims to provide an independent method for validating the physical location of a transmitting UAV against its reported telemetry data. This work is intended to contribute toward more secure and reliable UAV identification systems by exploring practical methods for detecting spoofed Remote ID broadcasts in real-world environments.
| Talk Length | 15 Minutes |
|---|---|
| Acknowledge | Acknowledge In-Person |