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With the overall momentum of the blockchain industry, financial crimes related to blockchain crypto-assets are becoming increasingly prevalent. After committing a crime, the main goal of cybercriminals is to obfuscate the source of the illicit funds in order to convert them into cash and get away with it. Many studies have analyzed money laundering (ML) in the field of the traditional financial sector. However, in terms of the emerging blockchain crypto-asset ecosystem, there is currently only one public anti-money laundering (AML) dataset for Bitcoin– the Elliptic dataset, whose binary labels (licit vs. illicit transactions) cannot cover the ML behaviors in the evergrowing crypto-asset market. To fill this gap, in this paper, we propose a framework named XBlockFlow which identifies ML addresses starting from Ethereum heist incidents and obtains the first detailed Ethereum ML dataset named EthereumHeist, and then conducts a comprehensive feature and evolution analysis on the EthereumHeist dataset according to the three main phases of ML. We first search for the source cybercriminal accounts including exchange hackers, DeFi exploiters, and scammers. Then, employing the idea of taint analysis, we track the diverse downstream transactions and addresses layer by layer. At the end of tracking, we identify and categorize service providers, and go a step further to investigate advanced ML methods that do not exist in the Bitcoin scenario, e. g. token swap and counterfeit token creation. Based on the ML identification results, we obtain many interesting findings about crypto-asset money laundering, observing the escalating money laundering methods such as creating counterfeit tokens and masquerading as speculators.
Wu et al. (Fri,) studied this question.