Correlation Analysis between Token Price and Liquidity for Fraud Detection in DeFi Ecosystems

Abstract

The rapid expansion of Decentralized Finance (DeFi) has enabled open and permissionless token trading, but it has also led to a surge in fraudulent activities such as rug pulls, wash trading, and pump-and-dump schemes. This paper presents a novel fraud detection approach based on correlation analysis between token price and liquidity, leveraging the inherent relationship between these two market variables. In legitimate markets, price movements are typically supported by corresponding changes in liquidity, whereas fraudulent tokens often exhibit abnormal or decoupled behavior due to artificial price manipulation. To investigate this, we analyze time-series data of token price and liquidity across multiple decentralized exchanges and compute statistical correlation metrics alongside liquidity variation patterns. Experimental results show that legitimate tokens maintain strong positive correlations (r > 0.7) between price and liquidity, while fraudulent tokens exhibit weak or unstable correlations (r < 0.3), often accompanied by sudden liquidity withdrawals or artificial volume spikes. The proposed framework achieves high detection performance with an accuracy of 92.4%, precision of 90.1%, recall of 93.6%, and F1-score of 91.8%, demonstrating its effectiveness in identifying suspicious tokens at early stages. The findings confirm that deviations in price–liquidity correlation serve as a reliable and computationally efficient indicator for fraud detection in DeFi ecosystems. This approach can be integrated with existing blockchain analytics tools to enhance real-time monitoring and improve investor protection.