Abstract
This paper proposes a method to predict transactions that are likely to be included in the next block from the mempool of unconfirmed transactions in the Bitcoin network. To implement the proposed method, we applied machine learning to the transactions data collected from the Bitcoin network and divided our implementation into the following three objects: Data Collector; Data Preprocessor; and Analyzer. We used the random forest classifier algorithm because the problem of predicting the likelihood of a transaction to be included in the next block is a binary classification problem. We evaluated the performance of our model by comparing transactions in the mempool against transaction published in the next two blocks mined at the time of our experiments. For both blocks, our model has a prediction accuracy of more than 80% and a minimal false negative error. The analysis of transaction inclusion in the next block is fundamental as it could drive the price of Bitcoin or signify the properties of a given transaction such as illegal or legal.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Nakamoto, S.: Bitcoin: A peer-to-peer electronic cash system (2008)
Swan, M.: Blockchain: Blueprint for a New Economy. O’Reilly Media, Inc., Sebastopol (2015)
Crosby, M., et al.: Blockchain technology: beyond bitcoin. Appl. Innov. 2(6–10), 71 (2016)
Bitcoin Wiki - Proof of work. https://en.bitcoin.it/wiki/Proof_of_work. Accessed 20 Sept 2019
O’Dwyer, K.J., David, M.: Bitcoin mining and its energy footprint, pp. 280–285 (2014)
Al-Shehabi, A.: Bitcoin transaction fee estimation using mempool state and linear perceptron machine learning algorithm (2018)
Fiz, B., Hommes, S.: Confirmation delay prediction of transactions in the bitcoin network. In: Park, J., Loia, V., Yi, G., Sung, Y. (eds.) CUTE 2017, CSA 2017. LNEE, vol. 474, pp. 534–539. Springer, Heidelberg (2017). https://doi.org/10.1007/978-981-10-7605-3_88
blockchain.info Explorer. https://www.blockchain.com/explorer. Accessed 15 Sept 2019
Pontiveros, B.B.F., Norvill, R., State, R.: Monitoring the transaction selection policy of Bitcoin mining pools. In: NOMS 2018–2018 IEEE/IFIP Network Operations and Management Symposium. IEEE (2018)
Bitcoin Core v0.17.1 Released. https://bitcoin.org/en/release/v0.17.1. Accessed 20 Sept 2019
Liaw, A., Wiener, M.: Classification and regression by randomForest. R news 2(3), 18–22 (2002)
Acknowledgments
This work was supported by the ICT R&D program of MSIT/IITP [No. 2018-000539, Development of Blockchain Transaction Monitoring and Analysis Technology] in Republic of Korea.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Ko, K., Jeong, T., Maharjan, S., Lee, C., Hong, J.WK. (2020). Prediction of Bitcoin Transactions Included in the Next Block. In: Zheng, Z., Dai, HN., Tang, M., Chen, X. (eds) Blockchain and Trustworthy Systems. BlockSys 2019. Communications in Computer and Information Science, vol 1156. Springer, Singapore. https://doi.org/10.1007/978-981-15-2777-7_48
Download citation
DOI: https://doi.org/10.1007/978-981-15-2777-7_48
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-15-2776-0
Online ISBN: 978-981-15-2777-7
eBook Packages: Computer ScienceComputer Science (R0)