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Tractor Beam: Safe-hijacking of Consumer Drones with Adaptive GPS Spoofing

Published: 09 April 2019 Publication History

Abstract

The consumer drone market is booming. Consumer drones are predominantly used for aerial photography; however, their use has been expanding because of their autopilot technology. Unfortunately, terrorists have also begun to use consumer drones for kamikaze bombing and reconnaissance. To protect against such threats, several companies have started “anti-drone” services that primarily focus on disrupting or incapacitating drone operations. However, the approaches employed are inadequate, because they make any drone that has intruded stop and remain over the protected area. We specify this issue by introducing the concept of safe-hijacking, which enables a hijacker to expel the intruding drone from the protected area remotely. As a safe-hijacking strategy, we investigated whether consumer drones in the autopilot mode can be hijacked via adaptive GPS spoofing. Specifically, as consumer drones activate GPS fail-safe and change their flight mode whenever a GPS error occurs, we performed black- and white-box analyses of GPS fail-safe flight mode and the following behavior after GPS signal recovery of existing consumer drones. Based on our analyses results, we developed a taxonomy of consumer drones according to these fail-safe mechanisms and designed safe-hijacking strategies for each drone type. Subsequently, we applied these strategies to four popular drones: DJI Phantom 3 Standard, DJI Phantom 4, Parrot Bebop 2, and 3DR Solo. The results of field experiments and software simulations verified the efficacy of our safe-hijacking strategies against these drones and demonstrated that the strategies can force them to move in any direction with high accuracy.

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Published In

cover image ACM Transactions on Privacy and Security
ACM Transactions on Privacy and Security  Volume 22, Issue 2
May 2019
214 pages
ISSN:2471-2566
EISSN:2471-2574
DOI:10.1145/3316298
Issue’s Table of Contents
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 09 April 2019
Accepted: 01 January 2019
Revised: 01 October 2018
Received: 01 May 2018
Published in TOPS Volume 22, Issue 2

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Author Tags

  1. Drone
  2. GPS spoofing
  3. anti-drone
  4. fail-safe

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  • Research-article
  • Research
  • Refereed

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  • Nano UAV Intelligence Systems Research Laboratory at Kwangwoon University
  • Agency for Defense Development (ADD)
  • Defense Acquisition Program Administration (DAPA)

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  • (2024)Diagnosis-guided Attack Recovery for Securing Robotic Vehicles from Sensor Deception AttacksProceedings of the 19th ACM Asia Conference on Computer and Communications Security10.1145/3634737.3644997(915-929)Online publication date: 1-Jul-2024
  • (2024)Covert attack design on UAV formation in dynamic target tracking tasks2024 39th Youth Academic Annual Conference of Chinese Association of Automation (YAC)10.1109/YAC63405.2024.10598437(225-230)Online publication date: 7-Jun-2024
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