One-Class Adversarial Nets for Fraud Detection


  • Panpan Zheng University of Arkansas
  • Shuhan Yuan University of Arkansas
  • Xintao Wu University of Arkansas
  • Jun Li University of Oregon
  • Aidong Lu University of North Carolina at Charlotte



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.




How to Cite

Zheng, P., Yuan, S., Wu, X., Li, J., & Lu, A. (2019). One-Class Adversarial Nets for Fraud Detection. Proceedings of the AAAI Conference on Artificial Intelligence, 33(01), 1286-1293.



AAAI Technical Track: Applications