Aparecium:understanding and detecting scam behaviors on Ethereum via biased random walk

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外文摘要：Ethereum's high attention,rich business,certain anonymity,and untraceability have attracted a group of attackers.Cybercrime on it has become increasingly rampant,among which scam behavior is convenient,cryptic,antagonistic and resulting in large economic losses.So we consider the scam behavior on Ethereum and investigate it at the node interaction level.Based on the life cycle and risk identification points we found,we propose an automatic detection model named Aparecium.First,a graph generation method which focus on the scam life cycle is adopted to mitigate the sparsity of the scam behaviors.Second,the life cycle patterns are delicate modeled because of the crypticity and antagonism of Ethereum scam behaviors.Conducting experiments in the wild Ethereum datasets,we prove Aparecium is effective which the precision,recall and F1-score achieve at 0.977,0.957 and 0.967 respectively.

外文关键词：

BlockchainNetwork securityEthereumScam detectionBehavior understanding

作者：

Chuyi Yan、Chen Zhang、Meng Shen、Ning Li、Jinhao Liu、Yinhao Qi、Zhigang Lu、Yuling Liu

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作者单位：

Institute of Information Engineering,Chinese Academy of Sciences,Beijing 100093,China

School of Cyber Security,University of Chinese Academy of Sciences,Beijing 100049,China

School of Cyberspace Science and Technology,Beijing Institute of Technology,Beijing 100081,China

基金：

National Key Research and Development Program of ChinaNational Key Research and Development Program of ChinaYouth Innovation Promotion Association CASStrategic Priority Research Program of Chinese Academy of SciencesNational Natural science Foundation of ChinaProgram of Key Laboratory of Network Assessment TechnologyChinese Academy of Sciences,Program of Beijing Key Laboratory of Network Security and Protection Technology

项目编号：

2021YFF03072032019QY13002021156XDC0204010061802404

出版年：

2024

DOI：

10.1186/s42400-023-00180-x

网络空间安全科学与技术(英文版)

ISSN：

年,卷(期)：2024.7(3)