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We characterize the advantage of using a robot's neighborhood to find andeliminate adversarial robots in the presence of a Sybil attack. We show that byleveraging the opinions of its neighbors on the trustworthiness of transmitteddata, robots can detect adversaries with high probability. We characterize anumber of communication rounds required to achieve this result to be a functionof the communication quality and the proportion of legitimate to maliciousrobots. This result enables increased resiliency of many multi-robotalgorithms. Because our results are finite time and not asymptotic, they areparticularly well-suited for problems with a time critical nature. We developtwo algorithms, \emph{FindSpoofedRobots} that determines trusted neighbors withhigh probability, and \emph{FindResilientAdjacencyMatrix} that enablesdistributed computation of graph properties in an adversarial setting. We applyour methods to a flocking problem where a team of robots must track a movingtarget in the presence of adversarial robots. We show that by using ouralgorithms, the team of robots are able to maintain tracking ability of thedynamic target.

Crowd Vetting: Rejecting Adversaries via Collaboration--with Application to Multi-Robot Flocking