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Distributed misbehavior detection in UAV flocks
Aguida, Mohamed Anis
Aguida, Mohamed Anis
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2023-07
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Thesis
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Electronic dissertations
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Abstract
Unmanned aerial vehicles have become increasingly popular in many applications
such as remote surveillance, reconnaissance, and precision agriculture. Often multiple
UAVs form a swarm and perform their operations in a distributed, coordinated fashion. A
rogue UAV in the flock can negatively disrupt the expected behavior and may jeopardize
the objective of the mission, leading other UAVs to make incorrect decisions or even crash.
This work introduces GRIFFIN, a distributed and lightweight misbehavior detection
framework for UAV flocks. GRIFFIN relies on readily available packet metadata (e.g., GPS
coordinates) and signal characteristics (e.g., RSSI measurements) and detects malicious
UAVs by employing a “majority voting” protocol. We show that GRIFFIN requires only
three honest nodes for correct operations. We implement and evaluate GRIFFIN on (a) a
realistic UAV simulator (ArduSim) and (b) a Raspberry Pi+Navio-based drone testbed.
We find that GRIFFIN outputs 100% successful detection with zero false negatives as long
as less than half of the UAVs in the flock are not compromised. Our implementation on the
real UAV testbed shows that runtime overhead of GRIFFIN is minimal (i.e., it requires less
than 1 MB of memory and consumes less than 1% of CPU) and computes operation within
2:5 ms.
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Thesis (M.S.)-- Wichita State University, College of Engineering, School of Computing
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Wichita State University
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© Copyright 2023 by Mohamed Anis Aguida
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