Hu Xinxin, Chen Haotian, Chen Hongchang, Li Xing, Zhang Junjie, Liu Shuxin
National Digital Switching System Engineering and Technological Research Center, Zhengzhou 450002, China.
The Edward S. Rogers Sr. Department of Electrical & Computer Engineering, University of Toronto, Toronto, ON M5S 3G4, Canada.
Entropy (Basel). 2023 Jan 11;25(1):150. doi: 10.3390/e25010150.
With the rapid evolution of mobile communication networks, the number of subscribers and their communication practices is increasing dramatically worldwide. However, fraudsters are also sniffing out the benefits. Detecting fraudsters from the massive volume of call detail records (CDR) in mobile communication networks has become an important yet challenging topic. Fortunately, Graph neural network (GNN) brings new possibilities for telecom fraud detection. However, the presence of the graph imbalance and GNN oversmoothing problems makes fraudster detection unsatisfactory. To address these problems, we propose a new fraud detector. First, we transform the user features with the help of a multilayer perceptron. Then, a reinforcement learning-based neighbor sampling strategy is designed to balance the number of neighbors of different classes of users. Next, we perform user feature aggregation using GNN. Finally, we innovatively treat the above augmented GNN as weak classifier and integrate multiple weak classifiers using the AdaBoost algorithm. A balanced focal loss function is also used to monitor the model training error. Extensive experiments are conducted on two open real-world telecom fraud datasets, and the results show that the proposed method is significantly effective for the graph imbalance problem and the oversmoothing problem in telecom fraud detection.
随着移动通信网络的迅速发展，全球范围内的用户数量及其通信行为正在急剧增加。然而，欺诈者也在觊觎其中的利益。从移动通信网络中大量的通话详单记录（CDR）中检测欺诈者已成为一个重要但具有挑战性的课题。幸运的是，图神经网络（GNN）为电信欺诈检测带来了新的可能性。然而，图不平衡和GNN过平滑问题的存在使得欺诈者检测效果不尽人意。为了解决这些问题，我们提出了一种新的欺诈检测器。首先，我们借助多层感知器对用户特征进行变换。然后，设计了一种基于强化学习的邻居采样策略来平衡不同类别用户的邻居数量。接下来，我们使用GNN进行用户特征聚合。最后，我们创新性地将上述增强后的GNN视为弱分类器，并使用AdaBoost算法集成多个弱分类器。还使用了平衡焦点损失函数来监测模型训练误差。在两个开放的真实世界电信欺诈数据集上进行了广泛的实验，结果表明所提出的方法对于电信欺诈检测中的图不平衡问题和过平滑问题具有显著的有效性。
