Applications and Security of Small Unmanned Aircraft Systems (sUASs)

Updated on 6/18/2022.

We use the term sUASs and consumer drones alternatively in the following. While sUASs have enabled many new applications, consumer drones have been abused in many recent attacks. One recent case was at the UK’s second largest airport. Malicious drones disturbed the normal operations during the busy 2018 Christmas season for three days. Clearly, we have to build effective solutions to stop such abuses.

Existing counter-drone solutions usually have two steps: we first identify an unauthorized drone entering a restricted airspace and then apply counter-drone solutions to disrupt or capture it. A typical setting is shown in Fig. 1. We set up a perimeter and apply drone detection schemes, using Radio Frequency (RF) communications, radars, acoustic monitoring, or image processing to discover incoming drones. We focus on the second step here: how to systematically counter the invading drones. In particular, in the no-fly zone, we assume that remote manual control is disabled and the malicious drone is on autopilot; we can apply the first-phase sensor attacks and the second-phase state estimation attacks to inject fake states into the autopilot navigation control. By examining the existing common autopilot algorithms, we propose several third-phase attacks to mislead invading drones.

Fig. 1 Attack Model

Most existing counter-drone systems have been proposed by industry with straightforward solutions, such as jamming drone control channels or GPS receivers to trigger a drone switching to a default failsafe mode (e.g., landing when lost GPS signals over 10s), or capturing a drone with a net, etc. Such direct physical attacks work well when dealing with unsophisticated drone operators; but they also show serious limitations, e.g., they usually do not consider collateral damages. If a drone carries a bomb, we should not make it land in a protected critical space, e.g., an office building. The best solution in such situation is to lead the drone fly

away from the target as far as possible. We have conducted a broad literature survey, and have not seen systematic research to address such issues. Therefore, it is urgent to investigate more intelligent counter-drone solutions.

Ideally, we want to precisely control the flight path of an unauthorized drone, e.g., making it miss its preset waypoints in its mission plan. In this paper, we consider attacking an autopiloted drone in three phases. The first-phase attacks focus on compromising the sensor readings of an autopiloted drone. Several first-phase attacks have shown that such attacks are completely feasible (e.g., GPS spoofing, compromising MEMS sensors). Based on such first-phase attacks, we have proposed the second-phase attacks, e.g., exploiting the weaknesses in common drone state estimation algorithms. Here, we focus on the third phase attacks by utilizing the compromised state estimation to fool common autopilot algorithms to make a drone deviate

from its flight paths. We do not assume that we can remotely tamper actuators.

Our goal is to compromise drone autopilot control algorithms to manipulate flight paths. We have conducted extensive investigation on popular open-source flight control systems (such as ArduPilot and Paparazzi), and discovered multiple weaknesses in common autopilot control algorithms. Because sensors often have occasional errors, drone control systems usually use state estimation algorithms to address

these errors. Kalman Filter (KF) and its variants are the most popular estimation algorithms in drone control systems. A drone autopilot system is dependent on these state estimations to adjust flight parameters. We have proposed several second phase attacks to manipulate state estimation algorithms in our previous papers. In this paper, we focus on exploiting the weakness of autopilot algorithms to manipulate a drone in

real time in order to make it follow (or not follow) certain flight paths, e.g., away from a target or missing certain points in a search sweep. To our best knowledge, we have not seen similar work in this direction.

Related Publications

    1. W. Chen, Z. Duan, and Y. Dong, “DPM: Towards Accurate Drone Position Manipulation,” IEEE Trans. on Dependable and Secure Computing. DOI: 10.1109/TDSC.2022.3144319. Page(s): 1 – 13, Jan, 2022. https://ieeexplore.ieee.org/document/9690021 (open access)

    2. Wenxin Chen, SECURITY INVESTIGATION OF DRONE CONTROL ALGORITHMS, PhD Dissertation, Dept. of Electrical and Computer Engineering, University of Hawaii, July 2021.

    3. Nguyen Banh, REAL-TIME STEALTH GPS SPOOFING ATTACKS ON CONSUMER DRONES. Master Thesis, Dept. of Electrical and Computer Engineering, University of Hawaii, May 2022.

    4. Jianqiu Cao, PRACTICAL GPS SPOOFING ATTACKS ON CONSUMER DRONES, Master Thesis, Dept. of Electrical and Computer Engineering, University of Hawaii, Dec. 2020.

    5. W. Chen, Z. Duan, and Y. Dong, “Compromising Flight Paths of Autopiloted Drones,” in Proc. of IEEE International Conference on Unmanned Aircraft Systems (ICUAS), June 11-14, 2019, Atlanta, USA.

    6. W. Chen, Y. Dong, and Z. Duan. “Manipulating Drone Position Control,” in Proc. of IEEE Conference on Computer Communications and Network Security (CNS’19), Washington, D.C., June 10-12, 2019.

    7. W. Chen, Y. Dong and Z. Duan, "Manipulating Drone Dynamic State Estimation to Compromise Navigation," 2018 IEEE Conference on Communications and Network Security (CNS), Beijing, 2018, pp. 1-9. DOI: 10.1109/CNS.2018.8433205URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8433205&isnumber=8433121

    8. W. Chen, Y. Dong and Z. Duan, "Attacking altitude estimation in drone navigation," IEEE INFOCOM 2018 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS), Honolulu, HI, 2018, pp. 888-893. DOI: 10.1109/INFCOMW.2018.8406948. URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8406948&isnumber=8406774

    9. W. Chen, Z. Duan and Y. Dong, "False data injection on EKF-based navigation control," 2017 International Conference on Unmanned Aircraft Systems (ICUAS), Miami, FL, USA, 2017, pp. 1608-1617. DOI: 10.1109/ICUAS.2017.7991406. URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7991406&isnumber=7991298