Security Investigation of Drone Control Algorithms
Date: Fri, June 25, 2021
Time: 10:00am - 12:00pm
Location: online, email for details
Speaker: Wenxin Chen, candidate for PhD, advisor Dr. Dong
While more and more autonomous vehicles and devices are deployed in our society, the security of these systems has become serious concerns. Although many efforts have focused on their performance and reliability, more systematical research on the security issues of their control algorithms are urgent and critical. Therefore, we focus on such issues in this dissertation. In particular, we select consumer drones as our subject because we can access open-source drone systems and conduct in-depth investigation 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 with the help of the current sensor measurement spoofing 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. We consider attacking an autonomous drone in three phases: attacking its onboard sensors, attacking its state estimation, and attacking its navigation algorithms. First, assume existing tools can help us accurately manipulate a drone's sensor measurements remotely; we first propose several False Data Injection (FDI) attacks to quantitatively control the EKF-based estimation of 2D horizontal position, altitude, and magnetic states, and conduct comprehensive analyses on the proposed attacks.
Furthermore, based on the ability of precisely modifying a drone's sensor measurements and state estimation, we developed the Drone Position Manipulation (DPM) attack, which 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 have formally analyzed 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.
Because the weaknesses investigated in this dissertation are common in many autonomous systems, the proposed attacks have much broad impacts to secure these systems. We will further develop countermeasures based on resource constraints to address these issues while ensuring system performance.