基于多特征融合的区块链异常交易检测

doi:10.3969/j.issn.1671-1122.2022.10.004

摘要/Abstract

摘要：

随着区块链技术的发展，以比特币为代表的虚拟货币已成为洗钱、黑客攻击、电信网络诈骗等犯罪行为的重要工具，给公民人身和财产安全带来了严重威胁，甚至威胁到国家金融市场的稳定。因此，针对基于区块链技术的虚拟货币异常交易数据检测的研究具有重要的意义。文章首先使用自定义的滑动窗口机制提取区块链交易数据特征；然后根据区块链交易数据的特点，从3个通道把数据处理成3个向量；最后对这3个特征向量进行拼接，构建区块链异常交易数据检测模型。文章使用区块链情报公司Elliptic发布的数据集验证模型的可行性和优越性，实验得出模型的准确率、召回率和F1值分别达到92.96%、85%和92.43%。实验结果表明，基于多特征融合的特征向量包含更加丰富的区块链交易信息，能够有效提升区块链异常交易检测的性能。

关键词: 区块链, 异常交易检测, 滑动窗口机制, 多特征融合

Abstract:

With the development of blockchain technology, virtual currency represented by bitcoin has become an important tool for money laundering, hacker attacks, telecommunications network fraud and other crimes, which poses a serious threat to the personal and property security of citizens, and even threatens the stability of the national financial market. Therefore, the research on abnormal transaction data detection of virtual currency based on blockchain technology is of great significance. Firstly, this paper use the custom sliding window mechanism to extract the characteristics of blockchain transaction data. Secondly, it procesed from three channels to form three feature vectors according to the characteristics of blockchain transaction data. Finally, it spliced these three feature vectors to build a blockchain abnormal transaction data detection model. This paper verified the feasibility and superiority of the model with the data set released by the blockchain intelligence company Elliptic. The precision, recall and F1 values of the model reached 92.96%, 85% and 92.43%. The experimental results show that the feature vector based on multi-feature fusion contains more abundant blockchain transaction information, which can effectively improve the performance of blockchain abnormal transaction detection.

Key words: blockchain, abnormal transaction detection, sliding window mechanism, multi-feature fusion

中图分类号:

TP309

林伟. 基于多特征融合的区块链异常交易检测[J]. 信息网络安全, 2022, 22(10): 24-30.

LIN Wei. Detection of Abnormal Transactions in Blockchain Based on Multi Feature Fusion[J]. Netinfo Security, 2022, 22(10): 24-30.

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实验环境	环境配置
CPU	Intel(R) Core(TM) i7-12700KF
GPU	GeForce RTX 3080 Ti
内存	32 GB
操作系统	Window 10操作系统
开发语言	Python 3.9.7
深度学习框架	PyTorch1.10.2

区块链交易集	类别	样本数
Elliptic数据集	合法	42019
	非法	4545
	未知	157509

模型	Precision	Recall	F1
KNN	74.52%	83.24%	78.24%
SVM	53.86%	59.28%	55.75%
决策树	84.71%	83.82%	84.26%
Adaboost模型	62.13%	76.17%	67.19%
MP-GCN^[6]	87.5%	61.2%	70.4%
DNN	92.16%	57.76%	88.8%
LSTM	92.52%	84.25%	91.94%
BiLSTM	92.56%	84.19%	92.07%
GRU	91.77%	82.28%	91.22%
BiGRU	92.06%	82.10%	91.62%
MFF	92.96%	85.00%	92.43%