Internet fraud transaction detection based on temporal-aware heterogeneous graph oversampling and attention fusion network

This study proposes an advanced Internet fraud transaction detection method, the Temporal-aware Heterogeneous Graph Oversampling and Attention Fusion Network (THG-OAFN), designed to address the increasingly severe fraud issues in EC. The method innovatively abstracts transaction data into a heterogeneous graph structure, captures temporal dynamic features through Gated Recurrent Unit (GRU), and fuses Graph Neural Network (GNN) to process static topological relationships. To address data imbalance, an improved Graph-based Synthetic Minority Oversampling Technique (GraphSMOTE) framework is introduced, maintaining the structural integrity of fraud clusters through k-hop topological constraints. Meanwhile, a multi-layer attention mechanism (including relationship fusion, neighborhood fusion, and information perception modules) is employed to achieve active fraud prevention. Experimental results show that THG-OAFN attains an area under the curve (AUC) of 96.56% (a 7.78% improvement over the best baseline). Moreover, it achieves a recall of 95.21% (a 6.29% improvement) and an F1-score of 94.72% (a 3.96% improvement) on the Amazon dataset. On the YelpChi dataset, these three metrics reach 90.43%, 89.51%, and 90.31%, respectively, remarkably outperforming existing GNN models. This achievement provides a deployable solution for dynamic fraud detection and active defense. Our code is available at https://github.com/wei4zheng/THG-OAFN.