Catching the Rug: Early Prediction of Fraudulent Memecoins on Solana via Machine Learning

2026-08-20

AI Summary

本文通过机器学习方法，特别是XGBoost模型，利用前5分钟交易数据早期预测Solana平台上的欺诈性迷因币（rug pull），并探讨了跨平台数据融合提升检测效果。

Abstract

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.

Problem

• memecoins
• rug pulls
• Solana
• fraud detection
• liquidity manipulation

Innovation

• early detection
• machine learning
• Solana
• rug pull
• multi-source data fusion