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Statistical detection of selfish mining in proof-of-work blockchain systems

Li, Sheng-Nan; Campajola, Carlo; Tessone, Claudio J (2024). Statistical detection of selfish mining in proof-of-work blockchain systems. Scientific Reports, 14(1):6251.

Abstract

The core of many cryptocurrencies is the decentralised validation network operating on proof-of-work technology. In these systems, validation is done by so-called miners who can digitally sign blocks once they solve a computationally-hard problem. Conventional wisdom generally considers this protocol as secure and stable as miners are incentivised to follow the behaviour of the majority. However, whether some strategic mining behaviours occur in practice is still a major concern. In this paper we target this question by focusing on a security threat: a selfish mining attack in which malicious miners deviate from protocol by not immediately revealing their newly mined blocks. We propose a statistical test to analyse each miner’s behaviour in five popular cryptocurrencies: Bitcoin, Litecoin, Monacoin, Ethereum and Bitcoin Cash. Our method is based on the realisation that selfish mining behaviour will cause identifiable anomalies in the statistics of miner’s successive blocks discovery. Secondly, we apply heuristics-based address clustering to improve the detectability of this kind of behaviour. We find a marked presence of abnormal miners in Monacoin and Bitcoin Cash, and, to a lesser extent, in Ethereum. Finally, we extend our method to detect coordinated selfish mining attacks, finding mining cartels in Monacoin where miners might secretly share information about newly mined blocks in advance. Our analysis contributes to the research on security in cryptocurrency systems by providing the first empirical evidence that the aforementioned strategic mining behaviours do take place in practice.

Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:03 Faculty of Economics > Department of Informatics
08 Research Priority Programs > Social Networks
Dewey Decimal Classification:000 Computer science, knowledge & systems
Scopus Subject Areas:Health Sciences > Multidisciplinary
Language:English
Date:15 March 2024
Deposited On:21 Jan 2025 10:53
Last Modified:28 Feb 2025 02:43
Publisher:Nature Publishing Group
ISSN:2045-2322
OA Status:Gold
Free access at:Publisher DOI. An embargo period may apply.
Publisher DOI:https://doi.org/10.1038/s41598-024-55348-3
Project Information:
  • Funder: Cardano Foundation
  • Grant ID:
  • Project Title:
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  • Language: English
  • Licence: Creative Commons: Attribution 4.0 International (CC BY 4.0)

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