ScamSweeper: Detecting Illegal Accounts in Web3 Scams via Transactions Analysis
Xiaoqi Li, Wenkai Li, Zhijie Liu, Meikang Qiu, Zhiquan Liu, Sen Nie, Zongwei Li, Shi Wu, Yuqing Zhang

TL;DR
ScamSweeper is a novel framework that leverages dynamic transaction graph analysis and a variational Transformer to effectively detect web3 scams on Ethereum, outperforming existing methods in large-scale transaction networks.
Contribution
The paper introduces ScamSweeper, a new approach that considers temporal and structural features of transaction graphs for improved scam detection in web3 applications.
Findings
Outperforms SIEGE, Ethident, and PDTGA with at least 17.29% F1-score improvement.
Achieves at least 17.5% better F1-score in phishing node detection over existing models.
Effectively analyzes large-scale Ethereum transaction data for scam identification.
Abstract
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. However, previous studies have primarily concentrated on de-anonymization and phishing nodes, neglecting the distinctive features of web3 scams. Moreover, 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. To overcome these challenges, we present ScamSweeper, a novel framework that emphasizes the dynamic evolution of transaction graphs, to identify web3 scams on Ethereum. ScamSweeper samples the network with a structure temporal random…
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Taxonomy
TopicsSpam and Phishing Detection · Blockchain Technology Applications and Security · Advanced Malware Detection Techniques
