通过多粒度洗交易模式分析提前预警BSC表情包代币的早期 Rug Pull
Early Rug Pull Warning for BSC Meme Tokens via Multi-Granularity Wash-Trading Pattern Profiling
AI总结：
本文提出一种针对BSC表情包代币的早期Rug Pull预警框架，通过多粒度洗交易模式特征建模，利用随机森林模型在有限数据下实现高精度预警，验证了多粒度洗交易特征的有效性。
摘要：
去中心化金融（DeFi）中表情包代币的高频发行和短周期投机显著放大了Rug Pull风险。现有方法在异常稀缺、标签不完整和可解释性有限的情况下仍难以提供稳定的早期预警。为此，本文提出一个端到端的预警框架，包含数据集构建与标注、洗交易模式特征建模、风险预测和错误分析四个阶段。方法上，基于三种洗交易模式（Self、Matched和Circular）构建12个代币级行为特征，将交易、地址和流动级信号统一为风险向量。监督模型用于输出预警分数和警报决策。在当前设置（7个代币，33,242条记录）下，随机森林在核心指标上优于逻辑回归，达到AUC=0.9098，PR-AUC=0.9185，F1=0.7429。消融结果表明，交易级特征是主要性能驱动因素（移除时PR-AUC下降0.1843），而地址级特征提供稳定的互补增益（移除时PR-AUC下降0.0573）。该模型在部分样本上展现出可操作的早期预警潜力，平均提前时间（v1）为3.8133小时。错误分析（FP=1，FN=8）表明，当前系统更适合作为高精度筛选器而非高召回率的自动报警引擎。主要贡献包括：可执行且可复现的Rug Pull预警流程、在弱监督下多粒度洗交易特征的实证验证，以及通过提前时间和误差界分析的部署导向证据。
英文摘要：
The high-frequency issuance and short-cycle speculation of meme tokens in decentralized finance (DeFi) have significantly amplified rug-pull risk. Existing approaches still struggle to provide stable early warning under scarce anomalies, incomplete labels, and limited interpretability. To address this issue, an end-to-end warning framework is proposed for BSC meme tokens, consisting of four stages: dataset construction and labeling, wash-trading pattern feature modeling, risk prediction, and error analysis. Methodologically, 12 token-level behavioral features are constructed based on three wash-trading patterns (Self, Matched, and Circular), unifying transaction-, address-, and flow-level signals into risk vectors. Supervised models are then employed to output warning scores and alert decisions. Under the current setting (7 tokens, 33,242 records), Random Forest outperforms Logistic Regression on core metrics, achieving AUC=0.9098, PR-AUC=0.9185, and F1=0.7429. Ablation results show that trade-level features are the primary performance driver (Delta PR-AUC=-0.1843 when removed), while address-level features provide stable complementary gain (Delta PR-AUC=-0.0573). The model also demonstrates actionable early-warning potential for a subset of samples, with a mean Lead Time (v1) of 3.8133 hours. The error profile (FP=1, FN=8) indicates that the current system is better positioned as a high-precision screener rather than a high-recall automatic alarm engine. The main contributions are threefold: an executable and reproducible rug-pull warning pipeline, empirical validation of multi-granularity wash-trading features under weak supervision, and deployment-oriented evidence through lead-time and error-bound analysis.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
