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

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  • Yinfeng Chen

    (School of Artificial Intelligence, Beijing Normal University, Beijing 100875, China
    School of Computer Information Management, Inner Mongolia University of Finance and Economics, Hohhot 010070, 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
    School of Computer Information Management, Inner Mongolia University of Finance and Economics, Hohhot 010070, China)

  • Rongfang Bie

    (School of Artificial Intelligence, Beijing Normal University, Beijing 100875, China)

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.

Suggested Citation

  • Yinfeng Chen & Yu Guo & Yaofei Wang & Rongfang Bie, 2022. "Toward Prevention of Parasite Chain Attack in IOTA Blockchain Networks by Using Evolutionary Game Model," Mathematics, MDPI, vol. 10(7), pages 1-19, March.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:7:p:1108-:d:782856
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    References listed on IDEAS

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    1. Li, Guihua & Zhen, Jin, 2005. "Global stability of an SEI epidemic model with general contact rate," Chaos, Solitons & Fractals, Elsevier, vol. 23(3), pages 997-1004.
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    Cited by:

    1. Chunxiao Li & Haodi Wang & Yu Zhao & Yuxin Xi & Enliang Xu & Shenling Wang, 2023. "Enabling High-Quality Machine Learning Model Trading on Blockchain-Based Marketplace," Mathematics, MDPI, vol. 11(12), pages 1-25, June.
    2. Yinfeng Chen & Yaofei Wang & Baojun Sun & Junxin Liu, 2023. "Addressing the Transaction Validation Issue in IOTA Tangle: A Tip Selection Algorithm Based on Time Division," Mathematics, MDPI, vol. 11(19), pages 1-17, September.

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