电子学报2023，Vol.51Issue(11)：3053-3060,8.DOI:10.12263/DZXB.20230489
基于噪声过滤与特征增强的图神经网络欺诈检测方法
Noise Filtering and Feature Enhancement Based Graph Neural Network Method for Fraud Detection
摘要
Abstract
Existing graph neural network(GNN)-based fraud detection methods have at least three shortcomings:(1)They do not adequately consider the problem of imbalanced distribution of sample labels.(2)They do not take into ac-count the problem that fraudsters deliberately create noise to interfere with fraud detection in order to avoid detection by de-tectors.(3)They fail to consider the limitations of sparse connections for fraud data.To address these three shortcomings,this paper proposes a fraud detection method,called NFE-GNN(Noise Filtering and feature Enhancement based Graph Neu-ral Network method for fraud detection),to improve the fraud detection performance.The proposed NFE-GNN method first employs a dataset-based fraud rate sampling technology to achieve a balance of benign and fraudulent samples.Based on this,a parameterized distance function is introduced to calculate the similarities between nodes,and the optimal noise fil-tering threshold is obtained through adaptive reinforcement learning.Finally,an effective algorithm is presented to increase the connections between fraudulent samples,and enrich the topology information in the graph to enhance the feature repre-sentation capability of fraudulent samples.The experimental results on two publicly available datasets demonstrate that the detection performance of the proposed NFE-GNN method is better than that of state-of-the-art graph neural network meth-ods.关键词
欺诈检测/类不平衡/节点分类/图结构数据/图神经网络/性能评估Key words
fraud detection/class imbalance/node classification/graph data/graph neural network/performance evaluation分类
信息技术与安全科学引用本文复制引用
李康和,黄震华..基于噪声过滤与特征增强的图神经网络欺诈检测方法[J].电子学报,2023,51(11):3053-3060,8.基金项目
国家自然科学基金(No.62172166) （No.62172166）
广东省基础与应用基础研究基金(No.2022A1515011380)National Natural Science Foundation of China(No.62172166) （No.2022A1515011380）
Guangdong Basic and Applied Basic Research Foundation(No.2022A1515011380) （No.2022A1515011380）
