A statistical method for detecting Bitcoin mining resource changes considering difficulty adjustments

【Author】 Seike, Hirotsugu; Aoki, Yasukazu; Koshizuka, Noboru

【Source】2024 IEEE INTERNATIONAL CONFERENCE ON OMNI-LAYER INTELLIGENT SYSTEMS, COINS 2024

【影响因子】

【Abstract】Bitcoin, which was launched in 2009, is one of the most popular blockchains. To sustain and secure the system, enough mining power must be needed. However, various factors can encourage miners to leave the Bitcoin network. This risk should be assessed in advance by introducing metrics to detect hash rate changes. For this purpose, this paper proposes a statistical method to determine whether there is a significant difference in computational power for the two given periods. Our proposal consists of three hypothesis tests that consider Bitcoin difficulty adjustments. The first and second tests detect hash rate changes in the mining resources across the Bitcoin network. The third focuses on shifts in the distribution of mining power among different pools. We conducted simulations to elucidate the statistical properties of the detection power of the first and second methods. In addition, we apply our three statistical tests to the Bitcoin block data at height 756,000 through 836,640 (from September 28, 2022 to March 28, 2024). Based on the inference results, we discuss how the mining power had fluctuated by some intervention effects, such as the drop in the Bitcoin price. This provides insights to understand and evaluate the stability of Bitcoin.

【Keywords】blockchain; distributed ledger technology; Bitcoin stability; mining incentives

【发表时间】2024

【收录时间】2024-10-21

【文献类型】理论模型

【主题类别】

区块链治理-技术治理-挖矿检测