Crowd Vetting: Rejecting Adversaries via Collaboration With Application to Multirobot Flocking - Mallmann-Trenn, Frederik; Cavorsi, Matthew; Gil, Stephanie (IEEE transactions on robotics, 2022)
In this article, 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 their neighbors on the trustworthiness of transmitted data, robots can detect adversaries with high probability. We characterize the number of communication rounds required to be a function of the communication quality and of the proportion of legitimate to malicious robots. This result enables increased resiliency of many multirobot algorithms. Because our results are finite time and not asymptotic, they are particularly well-suited for problems of a time critical nature. We develop two algorithms, FindSpoofedRobots that determines trusted neighbors with high probability, and 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.