Security of Small Unmanned Aircraft Systems (sUASs)

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 project, 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. 1. W. Chen*, Z. Duan, and Y. Dong, “Impacts of a Single GPS Spoofer on Multiple Receivers: Formal Analysis and Experimental Evaluation (PDF),” in Proc. of IEEE Consumer Communications & Networking Conference, Jan. 2024.

   2. D. Liang*, Y. Dong, “Identifying Consumer Drones Via Encrypted Traffic,” IUTAM Symposium on Optimal Guidance and Control for Autonomous Systems (ISOGCAS 2023), March 15-17, 2023.

   3. W. Chen*, Z. Duan, and Y. Dong, “Accurately Redirecting a Malicious Drone (PDF),” in Proc. of IEEE Consumer Communications & Networking Conference (CCNC), 2022.

   4. 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 PDF)

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

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

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

   8. 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.

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

   10. W. Chen, Y. Dong and Z. Duan, "Manipulating Drone Dynamic State Estimation to Compromise Navigation,(PDF)" 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

   11. W. Chen, Y. Dong and Z. Duan, "Attacking altitude estimation in drone navigation,(PDF)" 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

   12. W. Chen, Z. Duan and Y. Dong, "False data injection on EKF-based navigation control,(PDF)" 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