  We characterize the advantage of using a robot's neighborhood to find and
eliminate adversarial robots in the presence of a Sybil attack. We show that by
leveraging the opinions of its neighbors on the trustworthiness of transmitted
data, robots can detect adversaries with high probability. We characterize a
number of communication rounds required to achieve this result to be a function
of the communication quality and the proportion of legitimate to malicious
robots. This result enables increased resiliency of many multi-robot
algorithms. Because our results are finite time and not asymptotic, they are
particularly well-suited for problems with a time critical nature. We develop
two algorithms, \emph{FindSpoofedRobots} that determines trusted neighbors with
high probability, and \emph{FindResilientAdjacencyMatrix} that enables
distributed computation of graph properties in an adversarial setting. We apply
our methods to a flocking problem where a team of robots must track a moving
target in the presence of adversarial robots. We show that by using our
algorithms, the team of robots are able to maintain tracking ability of the
dynamic target.
