Detecting Rug Pulls in Decentralized Exchanges: Machine Learning Evidence from the TON Blockchain

• URL: http://arxiv.org/abs/2509.01168v1
• Date: Mon, 01 Sep 2025 06:39:50 GMT
• Title: Detecting Rug Pulls in Decentralized Exchanges: Machine Learning Evidence from the TON Blockchain
• Authors: Dmitry Yaremus, Jianghai Li, Alisa Kalacheva, Igor Vodolazov, Yury Yanovich,
• Abstract summary: 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.<n>We conduct a comprehensive study on the two largest TON DEXs, Ston.Fi and DeDust, fusing data from both platforms to train our models.<n>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.
• Score: 0.1522374059398944
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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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