Deep Structure Learning for Fraud Detection
Deep Structure Learning for Fraud Detection
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DOI：
10.1109/icdm.2018.00072
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发表时间：
2018-11
期刊：
影响因子：
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通讯作者：
Haibo Wang;Chuan Zhou;Jia Wu;Weizhen Dang;Xingquan Zhu;Jilong Wang
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文献类型：
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作者：
Haibo Wang;Chuan Zhou;Jia Wu;Weizhen Dang;Xingquan Zhu;Jilong Wang
欺诈检测非常重要，因为欺诈行为可能会误导消费者或给企业带来巨大损失。由于欺诈行为的锁步特征，欺诈检测问题可以被视为在归因二分图中寻找可疑的密集块。事实上，现有的基于属性的方法并不具有对抗鲁棒性，因为欺诈者可以采取一些伪装行为来掩盖他们正常的行为属性。更重要的是，现有的基于结构信息的方法仅考虑浅层拓扑结构，使其有效性对可疑块的密度敏感。在本文中，我们提出了一种新颖的深度结构学习模型 DeepFD 来区分正常用户和可疑用户。 DeepFD可以同时保留非线性图结构和用户行为信息。不同类型数据集的实验结果表明 DeepFD 优于最先进的基线。
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.