{"type":"ScholarlyArticle","title":"Don't Ignore Alienation and Marginalization: Correlating Fraud Detection","authors":[{"name":"Yilong Zang","slug":"yilong-zang","given":"Yilong","family":"Zang","affiliation":"National Engineering Research Center for Multimedia Software , School of Computer Science , Wuhan University"},{"name":"Ruimin Hu","slug":"ruimin-hu","given":"Ruimin","family":"Hu","affiliation":"National Engineering Research Center for Multimedia Software , School of Computer Science , Wuhan University"},{"name":"Zheng Wang","slug":"zheng-wang","given":"Zheng","family":"Wang","affiliation":"National Engineering Research Center for Multimedia Software , School of Computer Science , Wuhan University"},{"name":"Danni Xu","slug":"danni-xu","given":"Danni","family":"Xu","affiliation":"School of Computing , National University of Singapore"},{"name":"Jia Wu","slug":"jia-wu","given":"Jia","family":"Wu","affiliation":"School of Computing , Macquarie University"},{"name":"Dengshi Li","slug":"dengshi-li","given":"Dengshi","family":"Li","affiliation":"School of Artificial Intelligence , Jianghan University"},{"name":"Junhang Wu","slug":"junhang-wu","given":"Junhang","family":"Wu","affiliation":"National Engineering Research Center for Multimedia Software , School of Computer Science , Wuhan University"},{"name":"Lingfei Ren","slug":"lingfei-ren","given":"Lingfei","family":"Ren","affiliation":"National Engineering Research Center for Multimedia Software , School of Computer Science , Wuhan University"}],"abstract":"The anonymity of online networks makes tackling fraud increasingly costly. Thanks to the superiority of graph representation learning, graph-based fraud detection has made significant progress in recent years. However, upgrading fraudulent strategies produces more advanced and difficult scams. One common strategy is synergistic camouflage —— combining multiple means to deceive others. Existing methods mostly investigate the differences between relations on individual frauds, that neglect the correlation among multi-relation fraudulent behaviors. In this paper, we design several statistics to validate the existence of synergistic camouflage of fraudsters by exploring the correlation among multi-relation interactions. From the perspective of multi-relation, we find two distinctive features of fraudulent behaviors, i.e., alienation and marginalization. Based on the finding, we propose COFRAUD, a correlation-aware fraud detection model, which innovatively incorporates synergistic camouflage into fraud detection. It captures the correlation among multi-relation fraudulent behaviors. Experimental results on two public datasets demonstrate that COFRAUD achieves significant improvements over state-of-the-art methods.","doi":"10.24963/ijcai.2023/551","url":"https://ijcai.org/proceedings/2023/551","pdf":"https://ijcai.org/proceedings/2023/0551.pdf","markdown":"https://ijcai.org/proceedings/2023/551.md","bibtex":"https://ijcai.org/proceedings/2023/bibtex/551","volume":{"slug":"2023","title":"Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2023","url":"https://ijcai.org/proceedings/2023/","year":2023,"location":"Macao, SAR","dates":"19-25 August 2023","editors":"Edith Elkind","isbn":"978-1-956792-03-4","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2023"},"section":"Multidisciplinary Topics and Applications","track":"Main Track","number":551,"pages":{"first":4959,"last":4966},"keywords":["Data Mining: DM: Applications","Multidisciplinary Topics and Applications: MDA: Security and privacy"],"videos":[],"references":[{"position":1,"raw":"[Adamic and Adar, 2003] Lada A Adamic and Eytan Adar. 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