4.6 Article

The 51% Attack on Blockchains: A Mining Behavior Study

Journal

IEEE ACCESS
Volume 9, Issue -, Pages 140549-140564

Publisher

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/ACCESS.2021.3119291

Keywords

Blockchains; Bitcoin; Consensus algorithm; Computer science; Proof of Work; Proposals; Licenses; 51% attack; bitcoin; blockchain; double-speding; ethereum; hash rate

Funding

  1. Doctorado Nacional Convocatoria para la Formacion de Capital Humano de Alto Nivel para el Departamento de Boyaca
  2. Universidad del Norte

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This paper discusses the applications and potential 51% attack risks on blockchains. By studying the miners on Bitcoin and Ethereum blockchains, it is found that there is a concentration of hash rate and mining patterns among main miners, which poses a real risk for current blockchains. These findings provide a basis for detecting anomalous behaviors and preventing 51% attacks in the future.
The applications that use blockchain are cryptocurrencies, decentralized finance applications, video games, and many others. Most of these applications trust that the blockchain will prevent issues like fraud, thanks to the built-in cryptographic mechanisms provided by the data structure and the consensus protocol. However, blockchains suffer from what is called a 51% attack or majority attack, which is considered a high risk for the integrity of these blockchains, where if a miner, or a group of them, has more than half the computing capability of the network, it can rewrite the blockchain. Even though this attack is possible in theory, it is regarded as hard-achievable in practice, due to the assumption that, with enough active members, it is very complicated to have that much computing power; however, this assumption has not been studied with enough detail. In this work, a detailed characterization of the miners in the Bitcoin and Crypto Ethereum blockchains is presented, with the aim of proving the computing distribution assumption and creating profiles that may allow the detection of anomalous behaviors and prevent 51% attacks. The results of the analysis show that, in the last years, there has been an increasing concentration of hash rate power in a very small set of miners, which generates a real risk for current blockchains. Also, that there is a pattern in mining among the main miners, which makes it possible to identify out-of-normal behavior.

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