Toward Prevention of Parasite Chain Attack in IOTA Blockchain Networks by Using Evolutionary Game Model

Yinfeng Chen, Yu Guo, Yaofei Wang and Rongfang Bie
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Yinfeng Chen: School of Artificial Intelligence, Beijing Normal University, Beijing 100875, China
Yu Guo: School of Artificial Intelligence, Beijing Normal University, Beijing 100875, China
Yaofei Wang: School of Artificial Intelligence, Beijing Normal University, Beijing 100875, China
Rongfang Bie: School of Artificial Intelligence, Beijing Normal University, Beijing 100875, China

Mathematics, 2022, vol. 10, issue 7, 1-19

Abstract: IOTA is a new cryptocurrency system designed for the Internet of Things based on directed an acyclic graph structure. It has the advantages of supporting high concurrency, scalability, and zero transaction fees; however, due to the particularity of the directed acyclic graph structure, IOTA faces more complex security threats than the sequence blockchain, in which a parasite chain attack is a common double-spending attack. In this work, we propose a scheme that can effectively prevent parasite chain attacks to improve the security of the IOTA ledger. Our main idea is to analyze the behavior strategies of IOTA nodes based on evolutionary game theory and determine the key factors affecting the parasite chain attack and the restrictive relationship between them. Based on the above research, we provide a solution to resist the parasite chain attack and further prove the effectiveness of the scheme by numerical simulation. Finally, we propose the parasite chain attack prevention algorithms based on price splitting to effectively prevent the formation of the parasite chain.

Keywords: IOTA; parasite chain attack; the tangle; evolutionary game; security (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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