基于图卷积网络的比特币非法交易识别方法

doi:10.3969/j.issn.1671-1122.2021.09.011

信息网络安全 ›› 2021, Vol. 21 ›› Issue (9): 74-79.doi: 10.3969/j.issn.1671-1122.2021.09.011

基于图卷积网络的比特币非法交易识别方法

郑海潇¹^,², 文斌¹^,²()

1.海南师范大学信息科学技术学院,海口 571158
2.数据科学与智慧教育教育部重点实验室,海口 571158

收稿日期:2021-06-14 出版日期:2021-09-10 发布日期:2021-09-22
通讯作者: 文斌 E-mail:binwen@hainnu.edu.cn
作者简介:郑海潇（1998—）,男,河南,硕士研究生,主要研究方向为网络空间安全|文斌（1970—）,男,四川,博士,教授,主要研究方向为网络空间安全、区块链服务、大数据共享与交易。
基金资助:
国家自然科学基金(61562024);海南省自然科学基金(620RC605)

Bitcoin Illegal Transaction Identification Method Based on Graph Convolutional Network

ZHENG Haixiao¹^,², WEN Bin¹^,²()

1. School of Information Science and Technology, Hainan Normal University, Haikou 571158, China
2. Key Laboratory of Data Science and Smart Education of Ministry of Education, Haikou 571158, China

Received:2021-06-14 Online:2021-09-10 Published:2021-09-22
Contact: WEN Bin E-mail:binwen@hainnu.edu.cn

摘要/Abstract

摘要：

比特币作为匿名的加密数字资产逐渐成为部分非法地下交易的选择。为了净化金融市场、打击非法交易,需要对比特币网络中的非法交易活动进行识别。在相关工作的基础上,文章提出一种基于多层感知器与图卷积网络结合的检测比特币网络中非法交易的方法（Multi-layer Perceptrons + Graph Convolutional Network,MP-GCN）。MP-GCN使用多层感知器与图卷积网络组合,构建识别非法交易的模型。具体来说,在图卷积层之前和两层图卷积层中间用多层感知器进行辅助的特征提取,最后用线性层对非法交易进行划分。实验结果证明,MP-GCN达到的检测效果优于相同数据集的传统图卷积网络的检测效果。

关键词: 比特币网络, 非法交易识别, 图卷积网络

Abstract:

Bitcoin as an anonymous encrypted digital asset has gradually become the choice of some illegal underground transactions. To purify the financial market and combat illegal transactions, it is necessary to identify illegal transaction activities in the Bitcoin network. Based on related work, this article proposed a method for detecting illegal transactions in the Bitcoin network (Multi-layer Perceptrons + Graph Convolutional Network, MP-GCN) based on the combination of a multilayer perceptron and a graph convolutional network. MP-GCN used a combination of multi-layer perceptrons and graph convolutional networks to build a model for identifying illegal transactions. Specifically, before the graph convolutional layer and in the middle of the two-layer graph convolutional layer, a multi-layer perceptron was used for auxiliary feature extraction, and a linear layer was used to divide illegal transactions finally. The experimental results verify that the detection effect achieved by MP-GCN is better than that of the traditional graph convolutional network with the same data set.

Key words: bitcoin network, illegal transaction identification, graph convolutional network

中图分类号:

TP309

郑海潇, 文斌. 基于图卷积网络的比特币非法交易识别方法[J]. 信息网络安全, 2021, 21(9): 74-79.

ZHENG Haixiao, WEN Bin. Bitcoin Illegal Transaction Identification Method Based on Graph Convolutional Network[J]. Netinfo Security, 2021, 21(9): 74-79.

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基于图卷积网络的比特币非法交易识别方法

Bitcoin Illegal Transaction Identification Method Based on Graph Convolutional Network

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