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.

View More Papers

coucouArray ( [post_type] => ndss-paper [post_status] => publish [posts_per_page] => 4 [orderby] => rand [tax_query] => Array ( [0] => Array ( [taxonomy] => category [field] => id [terms] => Array ( [0] => 66 ) ) ) [post__not_in] => Array ( [0] => 13230 ) )

Exploiting Transport Protocol Vulnerabilities in SAE J1939 Networks

Rik Chatterjee, Subhojeet Mukherjee, Jeremy Daily (Colorado State University)

Read More

Hope of Delivery: Extracting User Locations From Mobile Instant...

Theodor Schnitzler (Research Center Trustworthy Data Science and Security, TU Dortmund, and Ruhr-Universität Bochum), Katharina Kohls (Radboud University), Evangelos Bitsikas (Northeastern University and New York University Abu Dhabi), Christina Pöpper (New York University Abu Dhabi)

Read More

Privacy-Preserving Database Fingerprinting

Tianxi Ji (Texas Tech University), Erman Ayday (Case Western Reserve University), Emre Yilmaz (University of Houston-Downtown), Ming Li (CSE Department The University of Texas at Arlington), Pan Li (Case Western Reserve University)

Read More

A Transcontinental Analysis of Account Remediation Protocols of Popular...

Philipp Markert (Ruhr University Bochum), Andrick Adhikari (University of Denver), Sanchari Das (University of Denver)

Read More

Privacy Starts with UI: Privacy Patterns and Designer Perspectives in UI/UX Practice

Anxhela Maloku (Technical University of Munich), Alexandra Klymenko (Technical University of Munich), Stephen Meisenbacher (Technical University of Munich), Florian Matthes (Technical University of Munich)

Vision: Profiling Human Attackers: Personality and Behavioral Patterns in Deceptive Multi-Stage CTF Challenges

Khalid Alasiri (School of Computing and Augmented Intelligence Arizona State University), Rakibul Hasan (School of Computing and Augmented Intelligence Arizona State University)

From Underground to Mainstream Marketplaces: Measuring AI-Enabled NSFW Deepfakes on Fiverr

Mohamed Moustafa Dawoud (University of California, Santa Cruz), Alejandro Cuevas (Princeton University), Ram Sundara Raman (University of California, Santa Cruz)