Credit Card Fraud Detection Based on Unsupervised Attentional Anomaly Detection Network

Credit Card Fraud Detection Based on Unsupervised Attentional Anomaly Detection Network

Blog Article

In recent years, with the rapid development of Internet technology, the number of credit card users has increased significantly.Subsequently, credit card fraud has caused a large amount of economic losses to individual users and related financial enterprises.At present, traditional machine learning methods (such as SVM, random forest, Markov model, etc.

) 2000 bmw 528i front bumper have been widely studied in credit card fraud detection, but these methods are often have difficulty in demonstrating their effectiveness when faced with unknown attack patterns.In this paper, a new Unsupervised Attentional Anomaly Detection Network-based Credit Card Fraud Detection framework (UAAD-FDNet) is proposed.Among them, fraudulent transactions turbo air m3f24-1-n are regarded as abnormal samples, and autoencoders with Feature Attention and GANs are used to effectively separate them from massive transaction data.

Extensive experimental results on Kaggle Credit Card Fraud Detection Dataset and IEEE-CIS Fraud Detection Dataset demonstrate that the proposed method outperforms existing fraud detection methods.