In recent years, blockchain technology has introduced decentralized finance (DeFi) as an alternative to traditional financial systems. DeFi aims to create a transparent and efficient financial ecosystem using smart contracts and emerging decentralized applications. However, the growing popularity of DeFi has made it a target for fraudulent activities, resulting in losses of billions of dollars due to various types of frauds. To address these issues, researchers have explored the potential of artificial intelligence (AI) approaches to detect such fraudulent activities. Yet, there is a lack of a systematic survey to organize and summarize those existing works and to identify the future research opportunities. In this survey, we provide a systematic taxonomy of various frauds in the DeFi ecosystem, categorized by the different stages of a DeFi project's life cycle: project development, introduction, growth, maturity, and decline. This taxonomy is based on our finding: many frauds have strong correlations in the stage of the DeFi project. According to the taxonomy, we review existing AI-powered detection methods, including statistical modeling, natural language processing and other machine learning techniques, etc. We find that fraud detection in different stages employs distinct types of methods and observe the commendable performance of tree-based and graph-related models in tackling fraud detection tasks. By analyzing the challenges and trends, we present the findings to provide proactive suggestion and guide future research in DeFi fraud detection. We believe that this survey is able to support researchers, practitioners, and regulators in establishing a secure and trustworthy DeFi ecosystem.

翻译：近年来，区块链技术引入了去中心化金融（DeFi）作为传统金融系统的替代方案。DeFi旨在利用智能合约和新兴的去中心化应用构建透明高效的金融生态系统。然而，DeFi日益增长的 popularity 使其成为欺诈活动的目标，各类欺诈行为已导致数十亿美元的损失。为应对这些问题，研究者探索了利用人工智能（AI）方法检测此类欺诈活动的潜力。然而，目前尚缺乏系统性的综述来组织总结现有研究并识别未来研究机遇。本综述对DeFi生态系统中的各类欺诈进行了系统分类，依据DeFi项目生命周期不同阶段进行划分：项目开发、引入、成长、成熟和衰退。该分类基于我们的发现：许多欺诈与DeFi项目所处阶段存在强相关性。根据这一分类，我们回顾了现有基于AI的检测方法，包括统计建模、自然语言处理及其他机器学习技术等。研究发现不同阶段的欺诈检测采用了差异化的方法类型，并观察到基于树和图的模型在欺诈检测任务中表现出色。通过分析挑战与趋势，我们提出前瞻性建议以指导DeFi欺诈检测的未来研究。本综述能够支持研究者、从业者和监管机构构建安全可信的DeFi生态系统。