用机器学习提前1小时预测Solana上山寨币跑路，保护投资者
Catching the Rug: Early Prediction of Fraudulent Memecoins on Solana via Machine Learning
- 仅用前5分钟交易数据，用梯度提升模型识别跑路风险
- 640万枚代币分析显示超90%在1小时内现跑路特征
- 跨平台数据融合可缓解领域差异，适合区块链风控应用
区块链平台上山寨币的快速泛滥增加了欺诈风险，尤其是跑路行为。尽管以往研究集中于以太坊，本文首次将焦点转向交易量与代币数量领先的Solana链。与以太坊依赖智能合约漏洞不同，Solana的跑路多源于流动性操控和社交动因。本研究构建了覆盖7个月、包含640万枚代币的大规模数据集，发现绝大多数山寨币在上线一小时内即表现出跑路特征，凸显短时预警的紧迫性。尽管缺乏代码级特征，仅使用前5分钟交易数据，经典机器学习模型（尤其是XGBoost）仍能有效检测潜在跑路。同时，跨平台（PumpFun与Raydium）评估表明，多源数据融合显著缓解领域偏移，提升检测可靠性。该研究深化了对高吞吐链上DeFi欺诈的理解，并提供了实用的投资者保护框架。
原文摘要 · Abstract (English)
The rapid proliferation of memecoins on blockchain platforms has increased the risk of fraudulent activities, particularly rug pulls. While previous studies have focused on Ethereum-based tokens, this paper shifts the spotlight to Solana, the leading blockchain for memecoins by trading volume and token count. Unlike Ethereum, where rug pulls often exploit smart contract backdoors, Solana memecoin rug pulls are predominantly driven by liquidity manipulation and social dynamics. This research pioneers large-scale rug pull early detection in the Solana ecosystem by assembling a dataset of 6.4 million tokens over 7 months. Market analysis reveals that a vast majority of these memecoins exhibit rug pull characteristics within one hour of launch, highlighting the urgency of short-horizon prediction. Despite the absence of code-level features, we demonstrate that classic machine learning models, particularly Gradient Boosting (XGBoost), achieve robust performance in detecting potential rug pulls using only the first 5 minutes of trading data. Furthermore, we evaluate cross-platform generalization between PumpFun and Raydium, revealing that multi-source data fusion significantly mitigates domain shift and improves detection reliability. This study advances the understanding of DeFi fraud on high-throughput chains and provides a practical framework for protecting investors.
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