{"type":"ScholarlyArticle","title":"Phishing Scam Detection on Ethereum: Towards Financial Security for Blockchain Ecosystem","authors":[{"name":"Weili Chen","slug":"weili-chen","given":"Weili","family":"Chen","affiliation":"School of Data and Computer Science , Sun Yat-sen University; National Supercomputer Center in Guangzhou , Sun Yat-sen"},{"name":"Xiongfeng Guo","slug":"xiongfeng-guo","given":"Xiongfeng","family":"Guo","affiliation":"School of Data and Computer Science , Sun Yat-sen University; National Engineering Research Center of Digital Life , Sun Yat-sen University"},{"name":"Zhiguang Chen","slug":"zhiguang-chen","given":"Zhiguang","family":"Chen","affiliation":"School of Data and Computer Science , Sun Yat-sen University; National Supercomputer Center in Guangzhou , Sun Yat-sen"},{"name":"Zibin Zheng","slug":"zibin-zheng","given":"Zibin","family":"Zheng","affiliation":"School of Data and Computer Science , Sun Yat-sen University; National Engineering Research Center of Digital Life , Sun Yat-sen University; University"},{"name":"Yutong Lu","slug":"yutong-lu","given":"Yutong","family":"Lu","affiliation":"School of Data and Computer Science , Sun Yat-sen University; National Supercomputer Center in Guangzhou , Sun Yat-sen"}],"abstract":"In recent years, blockchain technology has created a new cryptocurrency world and has attracted a lot of attention. It also is rampant with various scams. For example, phishing scams have grabbed a lot of money and has become an important threat to users' financial security in the blockchain ecosystem. To help deal with this issue, this paper proposes a systematic approach to detect phishing accounts based on blockchain transactions and take Ethereum as an example to verify its effectiveness. Specifically, we propose a graph-based cascade feature extraction method based on transaction records and a lightGBM-based Dual-sampling Ensemble algorithm to build the identification model. Extensive experiments show that the proposed algorithm can effectively identify phishing scams.","doi":"10.24963/ijcai.2020/621","url":"https://ijcai.org/proceedings/2020/621","pdf":"https://ijcai.org/proceedings/2020/0621.pdf","markdown":"https://ijcai.org/proceedings/2020/621.md","bibtex":"https://ijcai.org/proceedings/2020/bibtex/621","volume":{"slug":"2020","title":"Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2020","url":"https://ijcai.org/proceedings/2020/","year":2020,"location":"Yokohama","dates":"January 2021 (postponed from 11-17 July 2020)","editors":"Christian Bessiere","isbn":"978-0-9992411-6-5","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2020"},"section":"Special Track on AI in FinTech","track":"Special Track on AI in FinTech","number":621,"pages":{"first":4506,"last":4512},"keywords":["AI for banking: AI for digital currencies","AI for risk and security: AI for financial security","AI for banking: AI for cryptocurrencies"],"videos":[{"label":"Video","url":"https://videos.ijcai20.org/24693/file_24693.m3u8","length_sec":286,"provider":"hls"}],"references":[{"position":1,"raw":"[Abdelhamid et al., 2014] Neda Abdelhamid, Aladdin Ayesh, and Fadi Thabtah. 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