  This paper proposes a belief-updating scheme in a human-machine collaborative
decision-making network to combat Byzantine attacks. A hierarchical framework
is used to realize the network where local decisions from physical sensors act
as reference decisions to improve the quality of human sensor decisions. During
the decision-making process, the belief that each physical sensor is malicious
is updated. The case when humans have side information available is
investigated, and its impact is analyzed. Simulation results substantiate that
the proposed scheme can significantly improve the quality of human sensor
decisions, even when most physical sensors are malicious. Moreover, the
performance of the proposed method does not necessarily depend on the knowledge
of the actual fraction of malicious physical sensors. Consequently, the
proposed scheme can effectively defend against Byzantine attacks and improve
the quality of human sensors' decisions so that the performance of the
human-machine collaborative system is enhanced.
