With the proliferation of pump-and-dump schemes (P&Ds) in the cryptocurrency market, it becomes imperative to detect such fraudulent activities in advance to alert potentially susceptible investors. In this paper, we focus on predicting the pump probability of all coins listed in the target exchange before a scheduled pump time, which we refer to as the target coin prediction task. Firstly, we conduct a comprehensive study of the latest 709 P&D events organized in Telegram from Jan. 2019 to Jan. 2022. Our empirical analysis reveals some interesting patterns of P&Ds, such as that pumped coins exhibit intra-channel homogeneity and inter-channel heterogeneity. Here channel refers a form of group in Telegram that is frequently used to coordinate P&D events. This observation inspires us to develop a novel sequence-based neural network, dubbed SNN, which encodes a channel's P&D event history into a sequence representation via the positional attention mechanism to enhance the prediction accuracy. Positional attention helps to extract useful information and alleviates noise, especially when the sequence length is long. Extensive experiments verify the effectiveness and generalizability of proposed methods. Additionally, we release the code and P&D dataset on GitHub: https://github.com/Bayi-Hu/Pump-and-Dump-Detection-on-Cryptocurrency, and regularly update the dataset.

翻译：随着加密货币市场中拉高出货（P&D）骗局的泛滥，提前检测此类欺诈行为以警示潜在易感投资者变得至关重要。本文聚焦于在计划拉盘时间前预测目标交易所上市所有币种的拉盘概率，我们将此称为目标币种预测任务。首先，我们对2019年1月至2022年1月期间在Telegram上组织的709个最新拉高出货事件进行了全面研究。实证分析揭示了拉高出货的一些有趣模式，例如被拉盘币种呈现组内同质性与组间异质性（这里的"组"指Telegram中常用于协调拉高出货事件的群组形式）。这一发现启示我们开发了一种名为SNN的新型序列神经网络，它通过位置注意力机制将某个组的拉高出货事件历史编码为序列表示，从而提升预测精度。位置注意力有助于提取有效信息并抑制噪声，尤其在序列长度较长时效果显著。大量实验验证了所提方法的有效性和泛化能力。此外，我们在GitHub上发布了代码和拉高出货数据集（https://github.com/Bayi-Hu/Pump-and-Dump-Detection-on-Cryptocurrency），并定期更新数据集。