Catching the Rug: Early Prediction of Fraudulent Memecoins on Solana via Machine Learning· 通过机器学习预测欺诈性 memecoin
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 predict
通过机器学习预测 Solana 区块链上的欺诈性 memecoin
- 核心方法
- 构建了一个包含 640 万代币的大型数据集,利用机器学习模型分析 memecoin 的市场行为和社会动态,以早期检测 rug pull
- 适合谁读
- 区块链安全研究者、Solana 投资者、区块链经济学家
- 要解决的问题
- Solana 区块链上 memecoin 的欺诈行为(尤其是 rug pull)日益增加,如何在早期预测并识别这些欺诈行为
- 关键实验
- 构建了 640 万代币的数据集,分析了 memecoin 的市场行为和社会动态,展示了模型的预测性能
- 主要贡献
- 首次在 Solana 生态系统中实现大规模的 rug pull 早期检测,揭示了大部分 memecoin 在上线一小时内就表现出欺诈特征
- 意义与局限
- 对 Solana 生态系统的安全性和透明度有重要影响,有助于减少投资者损失,但模型的有效性和泛化能力需要进一步验证