Analyzing Transaction Confirmation in Ethereum Using Machine Learning Techniques

Abstract

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.

Análise de Características Estruturais de Tokens não Fungíveis no Ethereum

“…Em [Oliveira et al 2021] foi desenvolvido métodos de previsão de falha no processamento de contratos por mineradores do Ethereum utilizando classificadores com aprendizado supervisionado. Por sua vez, [Singh and Hafid 2019] desenvolveram modelos de ML para prever o tempo de confirmac ¸ão de uma transac ¸ão, explorando o impacto de classes de dados desbalanceadas no treinamento e teste dos modelos selecionados.…”

What makes Ethereum blockchain transactions be processed fast or slow? An empirical study

“…We use the data from this website to obtain transaction processing times (our dependent variable) and to engineer several features used in our model (e.g., pending pool size and network utilization level). Data from Etherscan has been used in several blockchain empirical studies ( [32], [33], [? ])).…”