Human-machine Hierarchical Networks for Decision Making under Byzantine Attacks

TL;DR

This paper introduces a hierarchical human-machine decision-making framework with belief updating to defend against Byzantine attacks, significantly improving decision quality even with malicious sensors.

Contribution

It presents a novel belief-updating scheme within a hierarchical network to enhance resilience against Byzantine attacks in human-machine decision systems.

Findings

Significantly improves decision quality under Byzantine attacks

Performance does not depend on the fraction of malicious sensors

Effective in scenarios with side information for humans

Abstract

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.