Distributed misbehavior detection in UAV flocks
Aguida, Mohamed Anis
AdvisorMonroy, Sergio A.Salinas
MetadataShow full item record
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.
Thesis (M.S.)-- Wichita State University, College of Engineering, School of Computing