We explored in this work the ubiquitous phenomenon of serial scammers, who deploy thousands of addresses to conduct a series of similar Rug Pulls on popular decentralized exchanges (DEXs). We first constructed a list of about 384,000 scammer addresses behind all 1-day Rug Pulls on the two most popular DEXs, Uniswap (Ethereum) and Pancakeswap (BSC), and identified many distinctive scam patterns including star-shaped, chain-shaped, and majority-flow scam clusters. We then proposed an algorithm to build a complete scam network from given scammer addresses, which consists of not only scammer addresses but also supporting addresses including depositors, withdrawers, transferrers, coordinators, and most importantly, wash traders. We note that profit estimations in existing works on Rug Pulls failed to capture the cost of wash trading, leading to inflated figures. Knowing who the wash traders are, we established a more accurate estimate for the true profit of individual scam pools as well as of the entire (serial) scam network by taking into account the wash-trading expenses.

翻译：本研究探讨了普遍存在的连环诈骗现象，即诈骗者部署数千个地址在主流去中心化交易所（DEX）上实施一系列相似的拉地毯骗局。我们首先构建了涵盖两大最流行DEX——Uniswap（以太坊）和Pancakeswap（币安智能链）上所有单日拉地毯骗局的约38.4万个诈骗地址列表，识别出包括星型、链型和主流向诈骗集群在内的多种典型诈骗模式。随后，我们提出一种从给定诈骗地址构建完整诈骗网络的算法，该网络不仅包含诈骗地址，还涵盖支持性地址，包括存款地址、提现地址、转账地址、协调地址，以及最重要的洗售交易地址。我们指出，现有拉地毯骗局研究中的利润估算未能计入洗售交易成本，导致数据虚高。通过识别洗售交易者，我们在考虑洗售交易支出的基础上，为单个诈骗资金池及整个（连环）诈骗网络建立了更精确的真实利润估算模型。