RugScreener: Leveraging Temporal Graph Neural Network for Rugpull Detection in DeFi

The advent of decentralized finance has ushered in a transformative era in the financial sector, leveraging blockchain technology to facilitate peer-to-peer transactions without traditional intermediaries. Amidst this innovation, the DeFi landscape faces the pervasive threat of rugpulls, where developers abruptly abandon projects post-fundraising, leaving investors with devalued assets. This growing concern highlights a critical research gap in the proactive detection and prevention of such fraudulent schemes. To combat this, we propose $\textsc{\textsf{RugScreener}}$ , a temporal graph neural network-based solution to identify rugpull risks within DeFi transactions. It employs a dynamic representation of blockchain interactions, enriched with comprehensive node attributes and effective temporal graph learning techniques based on memory and attention mechanisms, effectively capturing the rapid-moving and complex transaction patterns indicative of potential fraud. Our evaluation is based on a newly compiled Ethereum dataset that includes two subsets: an unlabeled set with 1,882,114 transactions from 29,595 tokens for temporal graph representation learning, and a labeled set with 128,819 transactions from 1,000 tokens (500 rugpull and 500 benign) for downstream evaluation. Using this dataset, $\textsc{\textsf{RugScreener}}$ achieves a balanced accuracy of 95.7% in detecting rugpull tokens. Remarkably, $\textsc{\textsf{RugScreener}}$ surpasses existing state-of-the-art graph learning models in detecting rugpull tokens with enhanced accuracy and reliability.