Understanding Selfish Mining in Imperfect Bitcoin and Ethereum Networks With Extended Forks

Autor: Hongyue Kang, Xiaolin Chang, Runkai Yang, Jelena Misic, Vojislav B. Misic
Rok vydání: 2021
Předmět:
Zdroj: IEEE Transactions on Network and Service Management. 18:3079-3091
ISSN: 2373-7379
DOI: 10.1109/tnsm.2021.3073414
Popis: Selfish mining, as a serious threat to blockchain, has been attracting attentions from academic and industry. Stochastic modeling has been explored to quantitatively investigate selfish mining in imperfect blockchain networks. However, prior modeling-based analysis approaches have some of the following issues: (1) only focus on Bitcoin or Ethereum, or (2) ignore extended forks and just consider natural forks, or (3) only compute the mining revenue without assessing the performance and security of the blockchain system when the system suffers from selfish mining. In this paper, we aim to address these issues. We build a Markov chain to make quantitative analysis of selfish mining in imperfect Bitcoin and Ethereum networks with natural and extended forks. Formulas are derived to calculate the mining revenue for the selfish pool (comprising selfish miners) and honest miners, respectively. Moreover, we derive the formulas of performance metrics (namely, transactions per second and stale block ratio) and the formula of security metric (namely, the probability of double-spending success) of the system. These quantitative results can help understand the impact of selfish mining on imperfect blockchain networks and then help the detection of selfish mining.
Databáze: OpenAIRE