[论文解读] Transaction Confirmation Time Prediction in Ethereum Blockchain Using Machine Learning

Harsh Jot Singh, Abdelhakim Hafid|arXiv (Cornell University)|Nov 25, 2019

Blockchain Technology Applications and Security参考文献 1被引用 7

一句话总结

本文提出一种基于机器学习的方法，利用区块级特征预测以太坊交易确认时间，对比了随机森林回归器与多层感知机模型与传统统计回归模型的性能。结果表明，机器学习模型在预测精度上显著优于传统统计方法。

ABSTRACT

Blockchain offers a decentralized, immutable, transparent system of records. It offers a peer-to-peer network of nodes with no centralised governing entity making it unhackable and therefore, more secure than the traditional paper-based or centralised system of records like banks etc. While there are certain advantages to the paper-based recording approach, it does not work well with digital relationships where the data is in constant flux. Unlike traditional channels, governed by centralized entities, blockchain offers its users a certain level of anonymity by providing capabilities to interact without disclosing their personal identities and allows them to build trust without a third-party governing entity. Due to the aforementioned characteristics of blockchain, more and more users around the globe are inclined towards making a digital transaction via blockchain than via rudimentary channels. Therefore, there is a dire need for us to gain insight on how these transactions are processed by the blockchain and how much time it may take for a peer to confirm a transaction and add it to the blockchain network. This paper presents a novel approach that would allow one to estimate the time, in block time or otherwise, it would take for a mining node to accept and confirm a transaction to a block using machine learning. The paper also aims to compare the predictive accuracy of two machine learning regression models- Random Forest Regressor and Multilayer Perceptron against previously proposed statistical regression model under a set evaluation criterion. The objective is to determine whether machine learning offers a more accurate predictive model than conventional statistical models. The proposed model results in improved accuracy in prediction.

研究动机与目标

• 开发一种基于机器学习的模型，用于预测以太坊区块链中的交易确认时间。
• 评估随机森林回归器与多层感知机模型相较于传统统计回归模型的预测性能。
• 确定机器学习技术是否在交易确认时间预测方面优于经典统计方法。
• 为用户和系统提供一种实用的、基于数据的工具，用于估算以太坊交易的确认延迟。

提出的方法

• 该模型使用历史以太坊区块数据，提取区块大小、交易数量、气体限制和气体价格等特征。
• 交易确认时间定义为从交易被包含到最终确认所经过的区块数，以区块时间计。
• 基于提取的特征，训练两种机器学习模型——随机森林回归器与多层感知机——以预测确认时间。
• 同时训练一个基线统计回归模型，并使用相同的数据集和评估标准进行对比。
• 使用标准回归指标（如平均绝对误差（MAE）、均方误差（MSE）和决定系数（R-squared））评估模型性能。
• 训练与评估基于从区块链收集的真实世界以太坊数据集完成。

实验结果

研究问题

• RQ1 机器学习模型是否能比传统统计回归模型更准确地预测以太坊交易确认时间？
• RQ2 在以太坊交易确认时间预测的背景下，随机森林回归器与多层感知机模型的预测性能如何比较？
• RQ3 哪组区块链特征能为准确的确认时间预测提供最具信息量的输入？
• RQ4 与统计模型相比，使用机器学习模型在预测精度上的相对提升是多少？

主要发现

• 随机森林回归器在所有模型中取得了最低的平均绝对误差（MAE），表明其具有更优的预测精度。
• 多层感知机模型在决定系数（R-squared）和均方误差（MSE）方面优于统计回归模型，显示出更好的拟合度与预测一致性。
• 两种机器学习模型相较于基线统计模型均显著降低了预测误差，证实了机器学习在此任务中的优势。
• 模型在不同网络条件下均表现出稳健性能，在高负载与低负载交易场景下均保持一致的预测精度提升。