用机器学习在TON链上提前5分钟识别币圈跑路骗局，效果显著。
Detecting Rug Pulls in Decentralized Exchanges: Machine Learning Evidence from the TON Blockchain
- 构建基于梯度提升的检测模型，结合流动性与交易活跃度双定义。
- TVL法AUC达0.891，5分钟内可有效识别跑路事件。
- 揭示跨平台数据融合难点，适合DeFi安全与投资者参考。
本文提出一种机器学习框架，用于在开放网络（TON）区块链的去中心化交易所（DEX）中早期检测跑路骗局。鉴于TON异步执行机制及来自Telegram的庞大Web2用户基础，其成为欺诈分析的新颖关键环境。研究聚焦于TON两大DEX——Ston.Fi与DeDust，融合双平台数据训练模型。创新性地在同一研究中对比了两种跑路定义：基于总锁仓价值（TVL）的灾难性流动性抽离，以及基于闲置活动的交易骤停。结果表明，梯度提升模型可在交易开始后五分钟内有效识别跑路，其中TVL方法最高AUC达0.891，而闲置方法在召回率上表现更优。分析发现，尽管特征集一致，但各交易所的分布差异显著，挑战直接数据融合，凸显需构建平台感知型鲁棒模型。本研究为投资者提供关键预警机制，并强化快速发展的TON DeFi生态安全体系。
原文摘要 · Abstract (English)
This paper presents a machine learning framework for the early detection of rug pull scams on decentralized exchanges (DEXs) within The Open Network (TON) blockchain. TON's unique architecture, characterized by asynchronous execution and a massive web2 user base from Telegram, presents a novel and critical environment for fraud analysis. We conduct a comprehensive study on the two largest TON DEXs, Ston.Fi and DeDust, fusing data from both platforms to train our models. A key contribution is the implementation and comparative analysis of two distinct rug pull definitions--TVL-based (a catastrophic liquidity withdrawal) and idle-based (a sudden cessation of all trading activity)--within a single, unified study. We demonstrate that Gradient Boosting models can effectively identify rug pulls within the first five minutes of trading, with the TVL-based method achieving superior AUC (up to 0.891) while the idle-based method excels at recall. Our analysis reveals that while feature sets are consistent across exchanges, their underlying distributions differ significantly, challenging straightforward data fusion and highlighting the need for robust, platform-aware models. This work provides a crucial early-warning mechanism for investors and enhances the security infrastructure of the rapidly growing TON DeFi ecosystem.
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