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http://dx.doi.org/10.13089/JKIISC.2019.29.1.225

Machine Learning Based Prediction of Bitcoin Mining Difficulty  

Lee, Joon-won (Information Security Lab, Graduation School of Information, Yonsei University)
Kwon, Taekyoung (Information Security Lab, Graduation School of Information, Yonsei University)
Publication Information
Journal of the Korea Institute of Information Security & Cryptology / v.29, no.1, 2019 , pp. 225-234 More about this Journal
Abstract
Bitcoin is a cryptocurrency with characteristics such as de-centralization and distributed ledger, and these features are maintained through a mining system called "proof of work". In the mining system, mining difficulty is adjusted to keep the block generation time constant. However, Bitcoin's current method to update mining difficulty does not reflect the future hash power, so the block generation time can not be kept constant and the error occurs between designed time and real time. This increases the inconsistency between block generation and real world and causes problems such as not meeting deadlines of transaction and exposing the vulnerability to coin-hopping attack. Previous studies to keep the block generation time constant still have the error. In this paper, we propose a machine-learning based method to reduce the error. By training with the previous hash power, we predict the future hash power and adjust the mining difficulty. Our experimental result shows that the error rate can be reduced by about 36% compared with the current method.
Keywords
Bitcoin; Mining difficulty; Time-series analysis; Predictive model; Machine learning;
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