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，区块链技术催生了去中心化金融（DeFi）作为传统金融体系的替代方案。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的日益普及使其成为欺诈活动的目标，由于多种类型的欺诈行为已导致数十亿美元的损失。为应对这些问题，研究者开始探索利用人工智能（AI）方法检测此类欺诈活动。然而，目前仍缺乏系统性的综述来整理与总结现有研究成果，并识别未来研究方向。本综述基于DeFi项目生命周期不同阶段（项目开发、引入、成长、成熟与衰退）对DeFi生态系统中的各类欺诈行为进行了系统分类。该分类源于我们的研究发现：许多欺诈行为与DeFi项目所处阶段具有强相关性。依据此分类，我们回顾了现有的基于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截然不同的方法类型，并观察到树模型和图相关模型在欺诈检测任务中表现优异。通过分析挑战与趋势，我们提出前瞻性建议，为DeFi欺诈检测的未来研究提供指引。我们相信，本综述能够支持研究人员、从业者和监管机构共同构建安全可信的DeFi生态系统。