{"title":"去中心化金融中的人工智能欺诈检测：项目生命周期视角","authors":"Bingqiao Luo, Zhen Zhang, Qian Wang, Anli Ke, Shengliang Lu, Bingsheng He","doi":"10.1145/3705296","DOIUrl":null,"url":null,"abstract":"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.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"65 1","pages":""},"PeriodicalIF":23.8000,"publicationDate":"2024-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"AI-powered Fraud Detection in Decentralized Finance: A Project Life Cycle Perspective\",\"authors\":\"Bingqiao Luo, Zhen Zhang, Qian Wang, Anli Ke, Shengliang Lu, Bingsheng He\",\"doi\":\"10.1145/3705296\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"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.\",\"PeriodicalId\":50926,\"journal\":{\"name\":\"ACM Computing Surveys\",\"volume\":\"65 1\",\"pages\":\"\"},\"PeriodicalIF\":23.8000,\"publicationDate\":\"2024-11-25\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"ACM Computing Surveys\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://doi.org/10.1145/3705296\",\"RegionNum\":1,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"COMPUTER SCIENCE, THEORY & METHODS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"ACM Computing Surveys","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1145/3705296","RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, THEORY & METHODS","Score":null,"Total":0}
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.
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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