Deep Structure Learning for Fraud Detection
- Publication Type:
- Conference Proceeding
- Citation:
- Proceedings - IEEE International Conference on Data Mining, ICDM, 2018, 2018-November pp. 567 - 576
- Issue Date:
- 2018-12-27
Closed Access
| Filename | Description | Size | |||
|---|---|---|---|---|---|
| 08594881.pdf | Published version | 1.59 MB |
Copyright Clearance Process
- Recently Added
- In Progress
- Closed Access
This item is closed access and not available.
© 2018 IEEE. Fraud detection is of great importance because fraudulent behaviors may mislead consumers or bring huge losses to enterprises. Due to the lockstep feature of fraudulent behaviors, fraud detection problem can be viewed as finding suspicious dense blocks in the attributed bipartite graph. In reality, existing attribute-based methods are not adversarially robust, because fraudsters can take some camouflage actions to cover their behavior attributes as normal. More importantly, existing structural information based methods only consider shallow topology structure, making their effectiveness sensitive to the density of suspicious blocks. In this paper, we propose a novel deep structure learning model named DeepFD to differentiate normal users and suspicious users. DeepFD can preserve the non-linear graph structure and user behavior information simultaneously. Experimental results on different types of datasets demonstrate that DeepFD outperforms the state-of-the-art baselines.
Please use this identifier to cite or link to this item: