One-Class Adversarial Nets for Fraud Detection

Understand

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 using training data with only benign users.