SECURITY INVESTIGATION OF DRONE CONTROL ALGORITHMS

Abstract

While more and more autonomous vehicles and devices are deployed in our society, the security of these systems has raised serious concerns. Although many efforts have focused on their performance and reliability, more systematic research on their security becomes urgent and critical. Therefore, we explore this direction and select consumer drones as our subjects because we can access open-source drone systems (i.e., ArduPilot systems) such that we are able to conduct in-depth investigations of their control algorithms in both theory and practice. As consumer drones have been abused in many incidents, protecting critical assets from consumer drone invasions has become increasingly challenging. While existing methods can interrupt an invading drone, none of them is able to accurately guide it to a desired location for safe handling. By exploiting the weaknesses identified in common state estimation methods and navigation algorithms of drones, and utilizing existing sensor attacking tools, in this research, we develop generic methods to compromise drone state estimation and position control in order to make malicious drones deviate from their targets. In general, an autonomous drone can be attacked at three levels: its onboard sensors, its state estimation, and its navigation algorithms. Our first focus is to accurately manipulate a drone’s state estimation by utilizing existing sensor attack tools. We propose several False Data Injection (FDI) attacks to quantitatively control the EKF-based estimation of 2-dimensional horizontal position, altitude, and magnetic states, and conduct comprehensive analyses on the proposed attacks. Our simulation results show the effectiveness of such attacks. We also propose countermeasures to deal with such attacks. Furthermore, we focus on the navigation algorithms and develop the Drone Position Manipulation (DPM) attack based on the ability of precisely attacking drone sensors and state estimation. DPM is able to accurately manipulate a drone’s physical position and help us guide an invading drone away from its target to a redirected destination. In addition, we formally analyze the feasible range of redirected destinations for a given target. The proposed attacks are validated on the popular ArduPilot flight control system to show its effectiveness. This unique method of exploiting the entire stack of sensing, state estimation, and navigation control together enables the quantitative manipulation of flight paths, different from all existing methods. We also discuss countermeasures to deal with such attacks and illustrate potential solutions. Because the weaknesses of common control algorithms investigated here are popular in many autonomous systems, the proposed attacks may also pose serious threats to the security of these systems. Utilizing different resources available on these autonomous systems, we are further investigating unique vulnerabilities and countermeasures in these environments while ensuring system performance.