Identifying Fraudulent Users in E-commerce Applications through Spatiotemporal Fusion and Selective Aggregation

The swift growth of e-commerce has led to an increase in fraudulent activities, which results in significant financial losses for both suppliers and consumers. Current research on detecting fraudulent activities within e-commerce platforms primarily focuses on analyzing individual user behavioral patterns over time or examining the spatial relationships among users. However, considering temporal or spatial contexts alone is not sufficient for fraud detection since they may not exist in real scenarios. Additionally, the issue caused by the imbalance of the data to be classified has not been solved in the field of fraud identification. To address these challenges, a novel scheme is proposed for fraudulent user detection in this work. The main contribution lies in the spatiotemporal fusion of user behavior and the layer-by-layer selective aggregation of graph models. Specifically, we utilize a long short-term memory model and a multi-layer perceptron model to extract the discriminant features from time-dependent and time-independent user behavior, respectively. This approach enhances the model’s ability to detect fraudulent users with different behavioral characteristics, including time-correlated and/or time-independent fraud behavior. Furthermore, a shared classifier is added to general graph neural network, it reclassifies the output of each layer of the graph model and reconstructs the spatial neighbor relationship. This little trick makes minority class samples select similar samples with a greater probability to build their spatial neighbor relationships, which can alleviate the issue of data imbalance. In the numerical experiments, three real datasets are used to validate the proposed scheme. Experiment results, including performance evaluation, comparison with existing benchmark approaches and ablation analysis, are presented and discussed.