One-Class Adversarial Nets for Fraud Detection

In this paper, we develop one-class adversarial nets (OCAN) for fraud detection with only benign users as training data.

Running Environment

The main packages you need to install

1. python 2.7 
2. tensorflow 1.3.0

DateSet

For experiments, we evaluate OCAN on two real-world datasets: twitter and wiki which have been attached in location.

Model Evaluation

The command line for OCAN goes as follow

    python oc_gan.py $1 $2
    
    where $1 refers to different datasets with wiki 1, credit-card(encoding) 2 and credit-card(plain) 3; 
          $2 denotes whether some metrics, such as fm_loss and f1 performed on training dataset, 
          are given or not in training process with non-display 0 and display 1.

where $1 refers to the corresponding distributions and it can be assigned as 'exp' (exponential), 'ray' (Rayleigh) and 'poi' (poisson); $2 denotes the datasets, 'twitter' or 'wiki'.

Authors

• Panpan Zheng

  • personal website: https://sites.uark.edu/pzheng/
  • google scholar: https://scholar.google.com/citations?user=f2OLKMYAAAAJ&hl=en

Reference

I am very glad that you could visit this github and check my research work. If it benefits for your work, please refer this work by .

Acknowledgments

This work was going on underlying the guide of prof. Xintao Wu(my advisor) and Dr. Shuhan Yuan(postdoc in our lab).

Appreciate it greatly for every labmate in SAIL lab in Uni. of Arkansas.