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A UAV Testbed for Diagnosing Hardware Vulnerabilities: Quantifying Sim-to-Real Discrepancies in PX4 Flight Logs.

Kubra Kose1, Jacob Wing1, Nuri Alperen Kose1

  • 1Department of Computer Science, Sam Houston State University, Huntsville, TX 77341, USA.

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Summary

This study introduces a UAV testbed for cybersecurity, comparing simulations with real flights to find critical differences in sensor data and hardware behavior. This helps detect cyber-physical threats in autonomous drones.

Keywords:
PX4 autopilotUAV testbedanomaly detection baselinecyber–physical securityflight data analysishardware vulnerabilitiessim-to-real gapuORB telemetry

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Area of Science:

  • Robotics and Control Systems
  • Cyber-Physical Systems Security
  • Aerospace Engineering

Background:

  • Autonomous Unmanned Aerial Vehicle (UAV) systems require robust security validation.
  • Existing simulation environments often fail to capture real-world hardware complexities and environmental factors.
  • Cyber-physical vulnerabilities in UAVs pose significant risks to safety-critical operations.

Purpose of the Study:

  • To establish a comprehensive UAV testbed for quantitative hardware vulnerability diagnosis.
  • To validate the cyber-physical security of autonomous UAVs by comparing simulation and real-world flight data.
  • To create a foundational framework for anomaly detection and security validation in UAV systems.

Main Methods:

  • Leveraging comparative flight logs from Software-In-The-Loop (SITL) simulations and real-world quadrotor missions.
  • Utilizing a unified data pipeline with the uORB message bus and ULog format for high-resolution telemetry extraction (IMU, state-estimation, actuator control).
  • Performing side-by-side time-series and statistical analyses of simulated versus real-world data across varying environmental conditions.

Main Results:

  • Identified critical sim-to-real discrepancies in sensor fidelity, GPS interference, and onboard resource behavior.
  • Quantified hardware-induced noise, mechanical vibrations, and electromagnetic disturbances impacting flight stability and reliability.
  • Established mathematical methods (variance, probability distribution shifts) to distinguish physical variability from anomalous or adversarial behavior.

Conclusions:

  • The developed UAV testbed provides a rigorous baseline for assessing cyber-physical security.
  • The findings highlight the necessity of real-world data for accurate vulnerability diagnosis and security validation.
  • This framework supports the development of robust anomaly detection models for secure autonomous UAV operations.