An investigation of the vulnerabilities of scale invariant dynamics in large teams

Large heterogeneous teams in a variety of applications must make joint decisions using large volumes of noisy and uncertain data. Often not all team members have access to a sensor, relying instead on information shared by peers to make decisions. These sensors can become permanently corrupted through hardware failure or as a result of the actions of a malicious adversary. Previous work showed that when the trust between agents was tuned to a specific value the resulting dynamics of the system had a property called scale invariance which led to agents reaching highly accurate conclusion with little communication. In this paper we show that these dynamics also leave the system vulnerable to most agents coming to incorrect conclusions as a result of small amounts of anomalous information maliciously injected in the system. We conduct an analysis that shows that the efficiency of scale invariant dynamics is due to the fact that large number of agents can come to correct conclusions when the difference between the percentage of agents holding conflicting opinions is relatively small. Although this allows the system to come to correct conclusions quickly, it also means that it would be easy for an attacker with specific knowledge to tip the balance. We explore different methods for selecting which agents are Byzantine and when attacks are launched informed by the analysis. Our study reveals global system properties that can be used to predict when and where in the network the system is most vulnerable to attack. We use the results of this study to design an algorithm used by agents to effectively attack the network, informed by local estimates of the global properties revealed by our investigation.