Blockchain-based cryptocurrencies,such as Bitcoins,are increasingly popular.However,the decentralized and anonymous nature of these currencies can also be(ab)used for nefarious activities such as money laundering,thus...Blockchain-based cryptocurrencies,such as Bitcoins,are increasingly popular.However,the decentralized and anonymous nature of these currencies can also be(ab)used for nefarious activities such as money laundering,thus reinforcing the importance of designing tools to effectively detect malicious transaction misbehaviors.In this paper,we propose TMAS,a transaction misbehavior analysis scheme for blockchain-based cryptocurrencies.Specifically,the proposed system includes ten features in the transaction graph,two heuristic money laundering models,and an analysis method for account linkage,which identifies accounts that are distinct but controlled by an identical entity.To evaluate the effectiveness of our proposed indicators and models,we analyze 100 million transactions and compute transaction features,and are able to identify a number of suspicious accounts.Moreover,the proposed methods can be applied to other cryptocurrencies,such as token-based cryptocurrencies(e.g.,Bitcoins)and account-based cryptocurrencies(e.g.,Ethereum).展开更多
基金supported by the Fujian Key Labo-ratory of Financial Information Processing(Putian University)(No.JXC202304)Yunnan Key Laboratory of Block-chain Application Tech-nology(No.202305AG340008)+1 种基金the Opening Project of Nanchang In-novation Institute,Peking University(No.NCII2022A02)Science and Technology Project of Putian City(No.2021R4001-10).The work of K.-K.R.Choo was supported only by the Cloud Technology Endowed Professorship.
文摘Blockchain-based cryptocurrencies,such as Bitcoins,are increasingly popular.However,the decentralized and anonymous nature of these currencies can also be(ab)used for nefarious activities such as money laundering,thus reinforcing the importance of designing tools to effectively detect malicious transaction misbehaviors.In this paper,we propose TMAS,a transaction misbehavior analysis scheme for blockchain-based cryptocurrencies.Specifically,the proposed system includes ten features in the transaction graph,two heuristic money laundering models,and an analysis method for account linkage,which identifies accounts that are distinct but controlled by an identical entity.To evaluate the effectiveness of our proposed indicators and models,we analyze 100 million transactions and compute transaction features,and are able to identify a number of suspicious accounts.Moreover,the proposed methods can be applied to other cryptocurrencies,such as token-based cryptocurrencies(e.g.,Bitcoins)and account-based cryptocurrencies(e.g.,Ethereum).
