论文标题
人群审查：通过协作拒绝对手 - 并应用多机器人羊群
Crowd Vetting: Rejecting Adversaries via Collaboration--with Application to Multi-Robot Flocking
论文作者
论文摘要
我们表征了在Sybil攻击存在下使用机器人邻居查找和消除对抗机器人的优势。我们表明，通过利用其邻居对传输数据的可信度的看法，机器人可以以很高的可能性来检测对手。我们表征了实现这一结果所需的许多沟通回合，这是沟通质量以及合法与恶意机器人的比例的函数。该结果使许多多机器人算法的弹性提高了。由于我们的结果是有限的时间，而不是渐近的时间，因此它们特别适合具有时间批判性质的问题。我们开发了两种算法，即\ emph {findspoofedrobots}，这些算法以高概率确定受信任的邻居，\ emph {findResilientAdjacencyMatrix}，该<emph {findResilientAdjacencyMatrix}启用了对抗性设置中图形属性的分布式计算。我们将方法应用于羊群问题，其中一组机器人必须在存在对抗机器人的情况下跟踪移动目标。我们表明，通过使用我们的算法，机器人团队能够保持动态目标的跟踪能力。
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.
