Joonha Jang (KAIST), ManGi Cho (KAIST), Jaehoon Kim (KAIST), Dongkwan Kim (Samsung SDS), Yongdae Kim (KAIST)

An inertial measurement unit (IMU) takes the key responsibility for the attitude control of drones. It is comprised of various sensors and transfers sensor data to the drones’ control unit. If it reports incorrect data, the drones cannot maintain their attitude and will crash down to the ground. Thus, several anti-drone studies have focused on causing significant fluctuations in the IMU sensor data by resonating the mechanical structure of the internal sensors using a crafted acoustic wave. However, this approach is limited in terms of efficacy for several reasons. As the structural details of each sensor in an IMU significantly differ by type, model, and manufacturer, the attack needs to be conducted independently for each sensor. Furthermore, it can be easily mitigated by using other supplementary sensors that are not corrupted by the attack or even with cheap plastic shielding.

In this paper, we propose a novel anti-drone technique that effectively corrupts ANY IMU sensor data regardless of the sensor’s type, model, and manufacturer. Our key idea is to distort the communication channel between the IMU and control unit of a drone by using an electromagnetic interference (EMI) signal injection. Experimentally, for a given control unit board, regardless
of the sensor used, we discovered a distinct susceptible frequency at which an EMI signal can greatly distort the sensor data. Compared to a general EM pulse (EMP) attack, it requires much less power as it targets the specific susceptible frequency. It can also avoid collateral damage from the EMP attack. For practical evaluation, we demonstrate the feasibility of the attack using real drones; the attack instantly paralyzed the drones. Lastly, we conclude by presenting practical challenges for its mitigation.

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Lightning Community Shout-Outs to:

(1) Jonathan Petit, Secure ML Performance Benchmark (Qualcomm) (2) David Balenson, The Road to Future Automotive Research Datasets: PIVOT Project and Community Workshop (USC Information Sciences Institute) (3) Jeremy Daily, CyberX Challenge Events (Colorado State University) (4) Mert D. Pesé, DETROIT: Data Collection, Translation and Sharing for Rapid Vehicular App Development (Clemson University) (5) Ning…

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Tommaso Frassetto (Technical University of Darmstadt), Patrick Jauernig (Technical University of Darmstadt), David Koisser (Technical University of Darmstadt), David Kretzler (Technical University of Darmstadt), Benjamin Schlosser (Technical University of Darmstadt), Sebastian Faust (Technical University of Darmstadt), Ahmad-Reza Sadeghi (Technical University of Darmstadt)

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