AI-URG: Account Identity-Based Uncertain Graph Framework for Fraud Detection
Journal
IEEE Transactions on Computational Social Systems
Date Issued
2023-01-01
Author(s)
Abstract
Cybercriminals controlling multiple accounts to conduct malicious activities are a threat to the security of online services. These accounts form malicious communities that are difficult to detect using conventional methods with single-factor identity, such as browser fingerprint or internet protocol (IP) address. Single-factor identity is prone to noise and uncertainty and does not capture dynamic relationships between accounts. To solve the problems of insufficient single-factor identity and uncertainty binding between accounts and identity, we propose Ai-Urg, a novel account identity-based uncertain graph with multifactor identity modeling for online service fraud detection. To find account groups, we embed account representation that preserves the uncertain graph’s possible world semantics and use the domain knowledge that accounts of family members also from small communities to filter these benign groups and detect malicious communities. The ablation study demonstrates how each component contributes to the effectiveness of Ai-Urg. The comparison results on two datasets show that Ai-Urg outperforms the alternatives with a higher F1-score (58.0%) and precision (46.0%) on a real-world online bank dataset and with a higher F1-score (36.5%) and precision (96.9%) on a large-scale single-sign-on online service dataset. The experimental results can provide valuable insights into online service fraud detection.
Subjects
Account identity relation | Browsers | Fingerprint recognition | Fraud | fraud detection | identity tracking | IP networks | node embedding | Object recognition | Security | uncertain graph | Uncertainty
Type
journal article