Ponzi schemes, a form of scam, have been discovered in Ethereum smart contracts in recent years, causing massive financial losses. Rule-based detection approaches rely on pre-defined rules with limited capabilities and domain knowledge dependency. Additionally, using static information like opcodes and transactions for machine learning models fails to effectively characterize the Ponzi contracts, resulting in poor reliability and interpretability.

In this paper, we propose PonziGuard, an efficient Ponzi scheme detection approach based on contract runtime behavior. Inspired by the observation that a contract’s runtime behavior is more effective in disguising Ponzi contracts from the innocent contracts, PonziGuard establishes a comprehensive graph representation called \textit{contract runtime behavior graph} (\textbf{CRBG}), to accurately depict the behavior of Ponzi contracts. Furthermore, it formulates the detection process as a graph classification task, enhancing its overall effectiveness. We conducted comparative experiments on a ground-truth dataset and applied PonziGuard to Ethereum Mainnet. The results show that PonziGuard outperforms the current state-of-the-art approaches and is also effective in open environments. Using PonziGuard, we have identified 805 Ponzi contracts on Ethereum Mainnet, which have resulted in an estimated economic loss of 281,700 Ether or approximately $500 million USD.