This study aims to detect pump and dump (P&D) manipulation in cryptocurrency markets, where the scarcity of such events causes severe class imbalance and hinders accurate detection. To address this issue, the Synthetic Minority Oversampling Technique (SMOTE) was applied, and advanced ensemble learning models were evaluated to distinguish manipulative trading behavior from normal market activity. The experimental results show that applying SMOTE greatly enhanced the ability of all models to detect P&D events by increasing recall and improving the overall balance between precision and recall. In particular, XGBoost and LightGBM achieved high recall rates (94.87% and 93.59%, respectively) with strong F1-scores and demonstrated fast computational performance, making them suitable for near real time surveillance. These findings indicate that integrating data balancing techniques with ensemble methods significantly improves the early detection of manipulative activities, contributing to a fairer, more transparent, and more stable cryptocurrency market.

翻译：本研究旨在检测加密货币市场中的拉盘砸盘操纵行为，此类事件的稀缺性导致严重的类别不平衡问题，阻碍了准确检测。为解决该问题，本研究应用了合成少数类过采样技术，并评估了先进的集成学习模型以区分操纵性交易行为与正常市场活动。实验结果表明，应用SMOTE技术通过提高召回率并改善精确率与召回率的整体平衡，显著增强了所有模型检测拉盘砸盘事件的能力。具体而言，XGBoost与LightGBM分别实现了高召回率（94.87%与93.59%），同时保持强劲的F1分数，并展现出快速的计算性能，使其适用于近实时监控。这些发现表明，将数据平衡技术与集成方法相结合，能显著提升对操纵活动的早期检测能力，有助于构建更公平、透明且稳定的加密货币市场。