APSIPA Transactions on Signal and Information Processing > Vol 11 > Issue 1

Integrating Human Decisions in the Presence of Byzantines: An Evolutionary Game Theoretical Approach

Yiqing Lin, Department of Automation, Tsinghua University, China, linyq20@mails.tsinghua.edu.cn , Hong Hu, Department of Automation, Tsinghua University, China, H. Vicky Zhao, Department of Automation, Tsinghua University, China, Yan Chen, School of Cyberspace Security, University of Science and Technology of China, China
 
Suggested Citation
Yiqing Lin, Hong Hu, H. Vicky Zhao and Yan Chen (2022), "Integrating Human Decisions in the Presence of Byzantines: An Evolutionary Game Theoretical Approach", APSIPA Transactions on Signal and Information Processing: Vol. 11: No. 1, e37. http://dx.doi.org/10.1561/116.00000035

Publication Date: 08 Dec 2022
© 2022 Y. Lin, H. Hu, H. V. Zhao and Y. Chen
 
Subjects
 
Keywords
Adversarial signal processingDecision fusionByzantine nodesGraphical evolutionary game theory
 

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This is published under the terms of CC BY-NC.

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In this article:
Introduction 
Problem Formulation 
Evolutionary Dynamics of the User Network with Byzantines 
Decision Fusion in the Affected Network by Byzantines 
Simulation Results 
Conclusions 
Appendix 
References 

Abstract

It is an established fact that malicious users in networks are able to mislead other users since the presence of herding behaviors, which will further amplify the hazards of these malicious behaviors. Due to the aforementioned scenarios in many practical applications, the study of decision fusion in the presence of such malicious users (often called Byzantines) is receiving increasing attention. In this paper, we propose an evolutionary game theoretical framework to model the human decision making process, which is based on the statistical signal processing framework. Specifically, we derive the analytical formulation of the evolutionary dynamics and the corresponding numerical evolutionary stable states, which can be utilized to infer the hazard of Byzantines on the network. Based on the above model and the Markov nature of the evolutionary dynamics, the fusion mechanism with maximum a posteriori estimation is proposed. Finally, simulation experiments are conducted to analyze the performance of the proposed human decision-making model and the effectiveness of the fusion mechanism under a variety of parameter settings.

DOI:10.1561/116.00000035