一种基于图神经网络的电信诈骗识别方法

A telecom fraud identification method based on graph neural network

通信技术的普及给人们带来便捷的同时,电信欺诈行为也急剧增加。由于诈骗行为特征、号码类型等与正常业务具有极高相似性,传统基于统计的电信欺诈检测方法难于筛选。提出将用户通信关系转换为一组拓扑特征,建立通信社交有向图,将具有统计特征的顶点表示用户,具有关系特征的边表示他们之间的活动。在通信社交图基础上,通过图卷积模块捕获用户的通信行为规律和通信社交关系特征,通过池化读出机制聚合通信社交网络的潜在特征,以识别电信欺诈行为。真实通信历史数据验证表明了该方法的有效性。

While communication technology brings convenience to people,telecom fraud also increases sharply.Traditional detection methods are mainly based on data mining and statistical learning of history data.However,due to the high similarity between fraud behavior and normal business,traditional statistical methods are difficult to screen.This paper proposes to transform user communication relationship into a set of topological features and establish communication social directed graph,where vertices with statistical characteristics represent users and edges with relational characteristics represent activities between them.On the basis of the communication social graph,the potential characteristics of the communication social network are learned through the graph neural network,and the information characteristics of multiple nodes are aggregated through pooling readout mechanism,in order to identify the telecom fraud users.The validation of real communication history data shows the effectiveness of this method.