Analyzing Transaction Confirmation in Ethereum Using Machine Learning Techniques
| Autor: | Heder S. Bernardino, Vinicius C. Oliveira, José Eduardo de Azevedo Sousa, Julia Almeida Valadares, Saulo Moraes Villela, Glauber Goncalves, Alex Borges Vieira |
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| Rok vydání: | 2021 |
| Předmět: |
Cryptocurrency
Computer Networks and Communications Computer science business.industry Machine learning computer.software_genre Relevant feature Hardware and Architecture Key (cryptography) Fraction (mathematics) Artificial intelligence business Area under the roc curve Database transaction computer Software |
| Zdroj: | ACM SIGMETRICS Performance Evaluation Review. 48:12-15 |
| ISSN: | 0163-5999 |
| DOI: | 10.1145/3466826.3466832 |
| Popis: | Ethereum has emerged as one of the most important cryptocurrencies in terms of the number of transactions. Given the recent growth of Ethereum, the cryptocurrency community and researchers are interested in understanding the Ethereum transactions behavior. In this work, we investigate a key aspect of Ethereum: the prediction of a transaction confirmation or failure based on its features. This is a challenging issue due to the small, but still relevant, fraction of failures in millions of recorded transactions and the complexity of the distributed mechanism to execute transactions in Ethereum. To conduct this investigation, we train machine learning models for this prediction, taking into consideration carefully balanced sets of confirmed and failed transactions. The results show high-performance models for classification of transactions with the best values of F1-score and area under the ROC curve approximately equal to 0.67 and 0.87, respectively. Also, we identified the gas used as the most relevant feature for the prediction. |
| Databáze: | OpenAIRE |
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