基于机器学习的比特币实体分类方法研究综述

A Review of Research on Machine Learning Based Bitcoin Entity Classification Methods

比特币是一种去中心化的电子加密货币,交易地址的匿名性隐藏了交易用户的真实身份,导致比特币被一些不法分子应用于各类非法活动中。通过分析已知实体的交易属性和行为特征,利用机器学习的方法可以对未知实体的交易类别进行预测。本文首先概述了比特币实体类别及分类标签的来源;其次,分析和归纳了基于机器学习的比特币实体分类方法;最后,分析了现阶段面临的主要问题,并对未来的发展趋势进行了展望。

Bitcoin is a decentralized electronic cryptocurrency, and the anonymity of the transaction address hides the real identity of the transaction user, leading to Bitcoin being used by some unscrupulous elements in various illegal activities.By analyzing the transaction attributes and behavioral characteristics of known entities, the category prediction of transactions of unknown entities can be performed by using machine learning methods. First, this paper outlines the sources of bitcoin entity categories and classification labels;second, it analyzes and summarizes the machine learning-based bitcoin entity classification methods;finally, it analyzes the problems faced at this stage and predicts the future development trends.