首页|Fraud calls detection using class‑imbalanced learning on graph structures
Fraud calls detection using class‑imbalanced learning on graph structures
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NETL
NSTL
Springer Nature
The rapid growth of mobile networks has enhanced social interactions but has also increased fraud risks in mobile social networks, leading to fnancial and economic losses. Fraud detection systems based on Graph Neural Networks (GNNs) utilize Call Detail Record (CDR) data to analyze social behaviors, yet they struggle with data imbalance, which limits their efectiveness. In this study, we address this chal- lenge by developing an improved minority class data augmentation approach for graph-based fraud detection. Building upon existing generative models, we enhance data generation using Wasserstein GAN with Gradient Penalty (WGAN-GP) to mitigate mode collapse and Deep Denoising Difusion Models (DDPM) to generate high-quality synthetic data. These synthetic samples are then integrated with graph- based classifers to improve fraud detection performance. Experimental results dem- onstrate that our approach signifcantly improves classifcation performance, par- ticularly in terms of F1-score, recall, and generalization across multiple graph-based fraud detection models. This research contributes to advancing data augmentation techniques for imbalanced graph data, ultimately enhancing fraud detection efec- tiveness and network security in mobile telecommunications.
NETL
NSTL
Springer Nature
