去中心化金融中的人工智能欺诈检测:项目生命周期视角

IF 23.8 1区 计算机科学 Q1 COMPUTER SCIENCE, THEORY & METHODS
Bingqiao Luo, Zhen Zhang, Qian Wang, Anli Ke, Shengliang Lu, Bingsheng He
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引用次数: 0

摘要

去中心化金融(DeFi)是一种新颖的金融系统,但也面临着巨大的欺诈挑战,导致大量损失。人工智能(AI)的最新进展显示了复杂欺诈检测的潜力。尽管人们对这些方法的兴趣与日俱增,但却缺乏对这些方法的系统回顾。本调查将欺诈类型与 DeFi 项目阶段相关联,提出了基于项目生命周期的分类法。我们对人工智能技术进行了评估,发现了基于树的模型和图相关模型的优越性等显著发现。基于这些见解,我们提出了建议并概述了未来的研究方向,以帮助研究人员、从业人员和监管机构提高 DeFi 的安全性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
AI-powered Fraud Detection in Decentralized Finance: A Project Life Cycle Perspective
Decentralized finance (DeFi) represents a novel financial system but faces significant fraud challenges, leading to substantial losses. Recent advancements in artificial intelligence (AI) show potential for complex fraud detection. Despite growing interest, a systematic review of these methods is lacking. This survey correlates fraud types with DeFi project stages, presenting a taxonomy based on the project life cycle. We evaluate AI techniques, revealing notable findings such as the superiority of tree-based and graph-related models. Based on these insights, we offer recommendations and outline future research directions to aid researchers, practitioners, and regulators in enhancing DeFi security.
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来源期刊
ACM Computing Surveys
ACM Computing Surveys 工程技术-计算机:理论方法
CiteScore
33.20
自引率
0.60%
发文量
372
审稿时长
12 months
期刊介绍: ACM Computing Surveys is an academic journal that focuses on publishing surveys and tutorials on various areas of computing research and practice. The journal aims to provide comprehensive and easily understandable articles that guide readers through the literature and help them understand topics outside their specialties. In terms of impact, CSUR has a high reputation with a 2022 Impact Factor of 16.6. It is ranked 3rd out of 111 journals in the field of Computer Science Theory & Methods. ACM Computing Surveys is indexed and abstracted in various services, including AI2 Semantic Scholar, Baidu, Clarivate/ISI: JCR, CNKI, DeepDyve, DTU, EBSCO: EDS/HOST, and IET Inspec, among others.
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