Rug pull detection on decentralized exchange using transaction data

Abstract

Cryptocurrency has transformed finance and investment, with platforms like Uniswap facilitating billions of dollars in trades. However, malicious smart contracts and scam tokens have led to significant financial losses for decentralized finance (DeFi) users. Code analysis alone cannot detect rug pulls using social engineering tactics. To address this issue, machine learning algorithms can leverage the vast amount of transactional data stored on the blockchain, particularly time series data, to identify scam tokens. This study aims to determine the optimal timeframe for detecting rug pulls and highlights the importance of token volume and transaction count features. The findings suggest that shorter timeframes are sufficient for detecting rug pull tokens since most incidents occur soon after token creation. This research offers new insights into scam token classification and prevention and contributes to a broader understanding of this field.