Detection of Malicious Ethereum Account Based on Time Series Transaction and Graph Attention Neural Network

doi:10.3969/j.issn.1671-1122.2022.10.010

Netinfo Security ›› 2022, Vol. 22 ›› Issue (10): 69-75.doi: 10.3969/j.issn.1671-1122.2022.10.010

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Detection of Malicious Ethereum Account Based on Time Series Transaction and Graph Attention Neural Network

SHI Tuo¹, LIANG Fei²(), SHANG Gangchuan², TIAN Yangjun³

1. Department of Public Security Management, Beijing Police College, Beijing 102202, China
2. Haidian Branch Police Support Brigade of Beijing Public Security Bureau, Beijing 100089, China
3. Public Security Bureau of Jinzhai County, Lu’an 237351, China

Received:2022-07-09 Online:2022-10-10 Published:2022-11-15
Contact: LIANG Fei E-mail:475662476@qq.com

Abstract

Abstract:

With the rapid development of blockchain, using ethereum to engage in pyramid selling, fraud, and money laundering crimes has increased year by year. Therefore, the detection of ethereum accounts has become an effective method to crack new types of crimes. The information was integrated into the characteristics of the ethereum address and account as a model to detect whether the account was a malicious one. The model in this paper improves the the neural network of graph attention mechanism and the time-series transaction information to realize the final expression of the address account characteristics. It is verified by experiments that the purposed model is superior to the graph neural network classification algorithm established by the traditional classification method.

Key words: graph attention mechanism, time kernel function, ethereum accounts

CLC Number:

TP309

SHI Tuo, LIANG Fei, SHANG Gangchuan, TIAN Yangjun. Detection of Malicious Ethereum Account Based on Time Series Transaction and Graph Attention Neural Network[J]. Netinfo Security, 2022, 22(10): 69-75.

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References 15

[1]	MA Chunguang, AN Jing, BI Wei, et al. Smart Contract in Blockchain[J]. Netinfo Security, 2018, 18(11): 8-17.
	马春光, 安婧, 毕伟, 等. 区块链中的智能合约[J]. 信息网络安全, 2018, 18(11): 8-17.
[2]	BIAN Lingyu, ZHANG Linlin, ZHAO Kai, et al. Ethereum Malicious Account Detection Method Based on LightGBM[J]. Netinfo Security, 2020, 20(4): 73-84.
	边玲玉, 张琳琳, 赵楷, 等. 基于LightGBM的以太坊恶意账户检测方法[J]. 信息网络安全, 2020, 20(4):73-84.
[3]	CHEN Weili, ZHENG Zibin, CUI Jiahui, et al. Detecting Ponzi Schemes on Ethereum: Towards Healthier Blockchain Technology[C]// Springer. Proceedings of the 2018 World Wide Web Conference. Heidelberg: Springer, 2018: 1409-1418.
[4]	TAO Feng, WANG Jinsong, LIU Duobin, et al. Bitcoin User Address Classification Method Based on Improved Random Forest[J]. Journal of Tianjin University of Technology, 2022, 38(1): 53-58.
	陶峰, 王劲松, 吕垛斌, 等. 基于改进随机森林的比特币用户地址分类方法[J]. 天津理工大学学报, 2022, 38(1): 53-58.
[5]	LIU Xiao, TANG Zaiyang, LI Peng, et al. A Graph Learning Based Approach for Identity Inference in DApp Platform Blockchain[J]. IEEE Transactions on Emerging Topics in Computing, 2022, 10(1): 438-449.
[6]	KANEZASHI H, SUZUMURAT, LIU Xin, et al. Ethereum Fraud Detection with Heterogeneous Graph Neural Networks[EB/OL]. (2022-07-04)[2022-07-08]. https://arxiv.org/abs/2203.12363.
[7]	VELICKOVIC P, CUCURULL G, CASANOVA A, et al. Graph Attention Networks[C]// Springer. International Conference on Learning Representations. Heidelberg: Springer, 2018: 1050-1062.
[8]	XU D, RUAN C, KORPROGLU E, et al. Inductive Representation Learning on Temporal Graphs[EB/OL]. (2020-10-19)[2022-07-08]. https://arxiv.org/abs/2002.07962.
[9]	ZHOU Jun, ZHANG Zhiqiang, CAO Yuetian, et al. A Geography and Time Aware Representation Learning Framework[J]. CAAI Transactions on Intelligent Systems, 2021, 16(5): 909-917.
	周俊, 张志强, 曹月恬, 等. 地理位置和时间感知的表示学习框架[J]. 智能系统学报, 2021, 16(5): 909-917
[10]	CHEN Liang, PENG Jiaying, LIU Yang, et al. Phishing Scams Detection in Ethereum Transaction Network[J]. ACM Transactions on Internet Technology, 2021, 21(1): 1-16.
[11]	ZHOU Jiajun, HU Chenkai, CHI Jianlei, et al. Behavior-Aware Account De-Anonymization on Ethereum Interaction Graph[EB/OL]. [2022-07-08]. https://arxiv.org/abs/2203.09360.
[12]	LI Panpan, XIE Yunyi, XU Xinyao, et al. Phishing Fraud Detection on Ethereum Using Graph Neural Network[EB/OL]. (2022-04-18)[2022-07-08]. https://arxiv.org/abs/2204.08194
[13]	KINGMA P, BA J. Adam: A Method for Stochastic Optimization[EB/OL]. (2017-01-30)[2022-07-08]. https://arxiv.org/abs/1412.6980.
[14]	LIANG Fei. Intelligent Detection Method of Malicious URL Based on Multi Group Attention Mechanism[J]. Netinfo Security, 2020, 20(1): 18-22.
	梁飞. 基于多分组注意力机制的恶意URL智能检测方法[J]. 信息网络安全, 2020, 20(1):18-22.
[15]	WU Jiajing, LIU Jieli, LIN Dan, et al. Blockchain Transaction Networks:A Survey[J]. Acta Scientiarum Naturalium Universitatis Sunyatseni, 2021, 60(5): 1-12.
	吴嘉婧, 刘洁利, 林丹, 等. 区块链交易网络研究综述[J]. 中山大学学报(自然科学版), 2021, 60(5):1-12.

检测算法	Precision	Recall	F1
LightGBM	82.63%	62.58%	71.22%
Graph-Attention	86.25%	68.32%	76.25%
Time-Graph-Att	88.76%	78.56%	83.35%

Detection of Malicious Ethereum Account Based on Time Series Transaction and Graph Attention Neural Network

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