Don't Ignore Alienation and Marginalization: Correlating Fraud Detection
Don't Ignore Alienation and Marginalization: Correlating Fraud Detection
Yilong Zang, Ruimin Hu, Zheng Wang, Danni Xu, Jia Wu, Dengshi Li, Junhang Wu, Lingfei Ren
Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence
Main Track. Pages 4959-4966.
https://doi.org/10.24963/ijcai.2023/551
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.
Keywords:
Multidisciplinary Topics and Applications: MDA: Security and privacy
Data Mining: DM: Applications