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Title: Compromising Flight Paths of Autopiloted Drones
While more and more consumer drones are abused in recent attacks, there is still very little systematical research on countering malicious consumer drones. In this paper, we focus on this issue and develop effective attacks to common autopilot control algorithms to compromise the flight paths of autopiloted drones, e.g., leading them away from its preset paths. We consider attacking an autopiloted drone in three phases: attacking its onboard sensors, attacking its state estimation, and attacking its autopilot algorithms. Several firstphase attacks have been developed (e.g., [1]–[4]); second-phase attacks (including our previous work [5], [6]) have also been investigated. In this paper, we focus on the third-phase attacks. We examine three common autopilot algorithms, and design several attacks by exploiting their weaknesses to mislead a drone from its preset path to a manipulated path. We present the formal analysis of the scope of such manipulated paths. We further discuss how to apply the proposed attacks to disrupt preset drone missions, such as missing a target in searching an area or misleading a drone to intercept another drone, etc. Many potential attacks can be built on top of the proposed attacks. We are currently investigating different models to apply such attacks on common drone missions and also building prototype systems on ArduPilot for real world tests. We will further investigate countermeasures to address the potential damages.  more » « less
Award ID(s):
1662487
NSF-PAR ID:
10127232
Author(s) / Creator(s):
; ;
Date Published:
Journal Name:
2019 International Conference on Unmanned Aircraft Systems (ICUAS)
Page Range / eLocation ID:
1316 to 1325
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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