One-Class Adversarial Nets for Fraud Detection
One-Class Adversarial Nets for Fraud Detection
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DOI：
10.1609/aaai.v33i01.33011286
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发表时间：
2018-03
期刊：
影响因子：
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通讯作者：
Panpan Zheng;Shuhan Yuan;Xintao Wu;Jun Yu Li;Aidong Lu
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文献类型：
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作者：
Panpan Zheng;Shuhan Yuan;Xintao Wu;Jun Yu Li;Aidong Lu
许多在线应用程序，如在线社交网络或知识库，经常受到恶意用户的攻击，这些用户会采取不同类型的行动，如在维基百科上进行破坏或在eBay上进行欺诈性评论。目前，大多数欺诈检测方法都需要包含良性和恶意用户记录的训练数据集。然而，在实践中，通常没有或很少有恶意用户的记录。在本文中，我们开发了一类对抗网络（OCAN），用于仅使用良性用户作为训练数据的欺诈检测。OCAN首先使用LSTM-Autoencoder从良性用户的在线活动序列中学习他们的表示。然后，它通过训练与常规GAN模型不同的互补GAN模型来检测恶意用户。实验结果表明，我们的OCAN优于最先进的一类分类模型，并与最新的多源LSTM模型实现了相当的性能，该模型在训练阶段需要良性和恶意用户。
Many online applications, such as online social networks or knowledge bases, are often attacked by malicious users who commit different types of actions such as vandalism on Wikipedia or fraudulent reviews on eBay. Currently, most of the fraud detection approaches require a training dataset that contains records of both benign and malicious users. However, in practice, there are often no or very few records of malicious users. In this paper, we develop one-class adversarial nets (OCAN) for fraud detection with only benign users as training data. OCAN first uses LSTM-Autoencoder to learn the representations of benign users from their sequences of online activities. It then detects malicious users by training a discriminator of a complementary GAN model that is different from the regular GAN model. Experimental results show that our OCAN outperforms the state-of-the-art oneclass classification models and achieves comparable performance with the latest multi-source LSTM model that requires both benign and malicious users in the training phase.