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Blockchain-based multi-malicious double-spending attack blacklist management model

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Abstract

In recent years, blockchain security issues have attracted widespread attention. Especially in terms of computing power security, when nodes have sufficient computing power, they can arbitrarily tamper with blockchain ledger data. As a huge collection of computing power, the blockchain mining pool is a potential malicious node. Multiple malicious nodes jointly conduct a double-spending attack on the blockchain, which brings great security risks to the blockchain. This paper studies the different combinations of double-spending attack by potential malicious mining pools, constructs a blacklist judgment strategy based on the attack characteristics, and proposes a blockchain-based multi-malicious double-spending attack blacklist management model. Firstly, an inner loop malicious game is proposed to analyze the competition trend between a single malicious node and other malicious nodes. Secondly, according to the attack mode of multiple mining pools, a joint attack model and a decentralized attack model are proposed, and the operation rules of the two attack models are mathematically derived. Then a blacklist management model is proposed, and after the blacklist is constructed, a blacklist judgment strategy is formulated. The blacklist uses decentralize mining pool judgments and joint mining pool judgments to extract malicious mining pool nodes, which stops further malicious behavior. And finally, simulation experiments show that the blacklist management model can significantly reduce the success of attacks and resist double-spending attacks effectively.

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Data availability

A significant amount of data is presented in this article. The remaining data that support the findings of this study are available on request from the corresponding author. The data are not publicly available due to privacy or ethical restrictions.

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Acknowledgements

The authors thank for prof. Linlin Ding and Dr Wanting Ji for helpful discussions.

Funding

This work was supported by the National Natural Science Foundation of China (61502215, 51704138), the Postdoctoral Science Foundation of China (2020M672134) and the Scientific Research Project of the Educational Department of Liaoning Province (LJC201913).

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  1. JunLu Wang and Qiang Liu contributed equally to this work.

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  1. School of Information, Liaoning University, Shenyang, 110036, China

    JunLu Wang, Qiang Liu & Baoyan Song

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  1. JunLu Wang
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Correspondence to Baoyan Song.

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Wang, J., Liu, Q. & Song, B. Blockchain-based multi-malicious double-spending attack blacklist management model. J Supercomput 78, 14726–14755 (2022). https://doi.org/10.1007/s11227-022-04370-1

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