One-Class Adversarial Nets for Fraud Detection

Abstract

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 one-class classification models and achieves comparable performance with the latest multi-source LSTM model that requires both benign and malicious users in the training phase.

Deep Learning for Anomaly Detection

“…Ref. [136] Tensorflow https://git.io/Jf4p4 6.4 OCAN [175] Tensorflow https://git.io/JfYGb 6.4 FenceGAN [105] Keras https://git.io/Jf4pR 6.4 OCGAN [121] MXNet https://git.io/Jf4p0 6.4…”

Study of Restrained Network Structures for Wasserstein Generative Adversarial Networks (WGANs) on Numeric Data Augmentation

“…Compared with GAN, the problem of the collapse mode is almost solved by WGAN, ensuring the diversity of the generated samples. Recently, GANs have been used to generate samples to improve classifier performance in credit card fraud detection [4]- [6] and other imbalanced datasets [20]. Zheng et al [7] adopted a deep denoising autoencoder to learn the complicated probabilistic relationship among the input features effectively and employed adversarial learning that established a min-max game between a discriminator and a generator to accurately discriminate between positive samples and negative samples in the data distribution.…”

“…the positive samples outnumber the negative samples or verse vice [3]. Nowadays, such GANs have been applied to the credit card fraud dataset [4]- [6] and the telecom fraud dataset [7].…”

“…1) The restrained structure can highly improve the performance of GANs. We compare restrained WGANs (SWGAN, MWGAN, IWGAN) with three other GANs: conventional WGAN [11], adapted GAN proposed in 2017 [6], and GAN-DAE in 2018 [4]. Besides, the most widely used oversampling method, SMOTE [8], and is employed in the evaluation.…”

Improving the security of image manipulation detection through one-and-a-half-class multiple classification

“…More recently, 1C classifiers have been used in conjunction with generative adversarial models (GANs) to design detectors with work under the assumption that no or very few instances of malicious samples are available for training. This is the case in [36], where the problem of forgery detection of satellite imagery is addressed, and in [38], with regard to general fraud detection, e.g. in reputation systems.…”