Detecting Malicious Accounts in Web3 through Transaction Graph

doi:10.1145/3691620.3695344

	Detecting Malicious Accounts in Web3 through Transaction Graph
	Li, Wenkai 1; Liu, Zhijie2 ; Li, Xiaoqi 1; Nie, Sen 3
	2024-10-27
会议录名称	PROCEEDINGS - 2024 39TH ACM/IEEE INTERNATIONAL CONFERENCE ON AUTOMATED SOFTWARE ENGINEERING, ASE 2024
ISSN	1938-4300
页码	2482-2483
发表状态	已发表
DOI	10.1145/3691620.3695344
摘要	The web3 applications have recently been growing, especially on the Ethereum platform, starting to become the target of scammers. The web3 scams, imitating the services provided by legitimate platforms, mimic regular activity to deceive users. The current phishing account detection tools utilize graph learning or sampling algorithms to obtain graph features. However, large-scale transaction networks with temporal attributes conform to a power-law distribution, posing challenges in detecting web3 scams. In this paper, we present ScamSweeper, a novel framework to identify web3 scams on Ethereum. Furthermore, we collect a large-scale transaction dataset consisting of web3 scams, phishing, and normal accounts. Our experiments indicate that ScamSweeper exceeds the state-of-the-art in detecting web3 scams. © 2024 Copyright is held by the owner/author(s). Publication rights licensed to ACM.
会议录编者/会议主办者	ACM ; ACM SIGAI ; Google ; IEEE ; Special Interest Group on Software Engineering (SIGSOFT) ; University of California, Davis (UC Davis)
关键词	Anonymity 'current Deep learning Detection tools Graph features Large-scale transactions Malicious account Phishing Sampling algorithm Transaction graph Web3 scam
会议名称	39th ACM/IEEE International Conference on Automated Software Engineering, ASE 2024
会议地点	Sacramento, CA, United states
会议日期	October 28, 2024 - November 1, 2024
URL	查看原文
收录类别	EI
语种	英语
出版者	Association for Computing Machinery, Inc
EI入藏号	20245117563826
EI主题词	Phishing
EI分类号	1106.2 ; 1108.1 ; 902.3 Legal Aspects
原始文献类型	Conference article (CA)
来源库	IEEE
文献类型	会议论文
条目标识符	https://kms.shanghaitech.edu.cn/handle/2MSLDSTB/461533
专题	信息科学与技术学院_硕士生
通讯作者	Li, Wenkai; Li, Xiaoqi
作者单位	1.Hainan University, Haikou, China; 2.ShanghaiTech University, Shanghai, China; 3.Keen Security Lab, Tencent, Shanghai, China
推荐引用方式 GB/T 7714	Li, Wenkai,Liu, Zhijie,Li, Xiaoqi,et al. Detecting Malicious Accounts in Web3 through Transaction Graph[C]//ACM, ACM SIGAI, Google, IEEE, Special Interest Group on Software Engineering (SIGSOFT), University of California, Davis (UC Davis):Association for Computing Machinery, Inc,2024:2482-2483.