<scp>TokenScout:</scp> Early Detection of Ethereum Scam Tokens via Temporal Graph Learning

TokenScout: раннее обнаружение мошеннических токенов Ethereum с помощью обучения на временных графах
Cong Wu, Jing Chen, Ziming Zhao, Kun He, Guowen Xu, Yueming Wu, Haijun Wang, Hongwei Li, Yang Liu, Yang Xiang
2024-12-02

Ethereum scam tokensTokenScoutearly detectionrugpullstemporal graph learning
Decentralized finance has experienced phenomenal growth, revolutionizing the landscape of financial transactions and asset management via blockchain. Yet, this swift growth brings with it substantial challenges, notably the surge in scam tokens, imposing significant security threats on cryptocurrency investments and trading. Existing detection methods of scam token, primarily relying on analyzing contract codes or transaction patterns, struggle to catch increasingly sophisticated tactics employed by scammers. For example, contract-based analysis are unable to identify scams lacking overt malicious code, e.g., most rugpulls, while transaction-based methods generally lack the foresight to early-detect potential risks.
1
Existing contract-code analyses miss scams without overt malicious code, including most rug pulls.
2
Scam tokens pose growing security risks in decentralized finance as cryptocurrency adoption and trading expand.
3
TokenScout is positioned as an early-detection approach using temporal graph learning to address limitations of contract- and transaction-based methods.
4
Transaction-pattern methods generally lack sufficient foresight for early detection of potential scam-token risks.

Ethereum scam tokens

Early detection of scam tokens and potential rugpull risks using temporal graph learning despite the limitations of contract-code and transaction-pattern analysis

Publication Details
Publication Date
2024-12-02
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Authors
Cong Wu
Jing Chen
Ziming Zhao
Kun He
Guowen Xu
Yueming Wu
Haijun Wang
Hongwei Li
Yang Liu
Yang Xiang
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