Publication | Open Access
<scp>TokenScout:</scp> Early Detection of Ethereum Scam Tokens via Temporal Graph Learning
34
Citations
26
References
2024
Year
Unknown Venue
Fraud DetectionEngineeringMachine LearningSwift GrowthInformation SecurityNetwork AnalysisInformation ForensicsCryptocurrencyFintechData ScienceData MiningAsset ManagementEthereum Scam TokensThreat DetectionBlockchain SecurityKnowledge DiscoveryComputer ScienceScam TokenSmart ContractFinanceData SecurityCryptographyNetwork ScienceGraph TheoryTemporal Graph LearningMoney LaunderingTransaction Graph AnalysisBusinessBotnet DetectionTemporal NetworkFinancial EngineeringGraph AnalysisBlockchain
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.
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