一句话：提出基于合约运行时行为图（CRBG）的以太坊庞氏骗局检测方法 PonziGuard，以提升检测可靠性与可解释性。
以太坊智能合约中的庞氏骗局造成严重财务损失。现有基于规则的检测依赖预定义规则，能力有限且依赖领域知识；而使用 opcode、交易等静态信息训练机器学习模型，难以有效刻画庞氏合约，导致可靠性与可解释性较差。本文提出 PonziGuard，其核心是合约运行时行为图（CRBG），试图从合约运行行为角度表征庞氏骗局并进行检测。摘要未给出具体图构造、模型细节及实验指标，因此关键结果尚不明确。
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.
DOI 原文 ·
@inproceedings{paperbot11269,
title = {PonziGuard: Detecting Ponzi Schemes on Ethereum with Contract Runtime Behavior Graph (CRBG)},
author = {Ruichao Liang and Jing Chen and Kun He and Yueming Wu and Gelei Deng and Ruiying Du and Cong Wu},
booktitle = {ICSE},
year = {2024},
doi = {10.1145/3597503.3623318}
}