Towards Effective Detection of Ponzi schemes on Ethereum with Contract Runtime Behavior Graph

• URL: http://arxiv.org/abs/2406.00921v1
• Date: Mon, 3 Jun 2024 01:17:48 GMT
• Title: Towards Effective Detection of Ponzi schemes on Ethereum with Contract Runtime Behavior Graph
• Authors: Ruichao Liang, Jing Chen, Cong Wu, Kun He, Yueming Wu, Weisong Sun, Ruiying Du, Qingchuan Zhao, Yang Liu,
• Abstract summary: Ponzi schemes, a form of scam, have been discovered in smart contracts in recent years, causing massive financial losses. Existing detection methods primarily focus on rule-based approaches and machine learning techniques. We propose PonziGuard, an efficient Ponzi detection approach based on contract runtime behavior.
• Score: 17.79695486585971
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Ponzi schemes, a form of scam, have been discovered in Ethereum smart contracts in recent years, causing massive financial losses. Existing detection methods primarily focus on rule-based approaches and machine learning techniques that utilize static information as features. However, these methods have significant limitations. Rule-based approaches rely on pre-defined rules with limited capabilities and domain knowledge dependency. Using static information like opcodes for machine learning fails to effectively characterize Ponzi contracts, resulting in poor reliability and interpretability. Moreover, relying on static information like transactions for machine learning requires a certain number of transactions to achieve detection, which limits the scalability of detection and hinders the identification of 0-day Ponzi schemes. 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 contract runtime behavior graph (CRBG), to accurately depict the behavior of Ponzi contracts. Furthermore, it formulates the detection process as a graph classification task on CRBG, enhancing its overall effectiveness. The experiment results show that PonziGuard surpasses the current state-of-the-art approaches in the ground-truth dataset. We applied PonziGuard to Ethereum Mainnet and demonstrated its effectiveness in real-world scenarios. Using PonziGuard, we identified 805 Ponzi contracts on Ethereum Mainnet, which have resulted in an estimated economic loss of 281,700 Ether or approximately $500 million USD. We also found 0-day Ponzi schemes in the recently deployed 10,000 smart contracts.

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