关键词: 诈骗电话识别, 图, 多层感知机, 卷积神经网络, 多通道注意力机制

Abstract: A novel fraud call recognition model named GLPMAM was proposed to address the problem that the correlation between users is rarely considered in traditional fraud call recognition models. The call-making relationships between phone users were modeled as a graph to consider the correlation between them, and user features were extracted by combining multi-layer perceptron (MLP) and convolutional neural network (CNN). Finally, the multi-channel attention mechanism was applied to further improve the accuracy. Experiments on public datasets provided by the first Digital Sichuan Innovation Competition showed that the accuracy of GLPMAM reached over 99%, which was 7% higher than the existing fraud call recognition model. The ablation experiment verified the important role of graph construction in improving accuracy. Experimental results demonstrated that GLPMAM could effectively improve the accuracy of fraud call recognition in practical applications, and the model running speed could adequately meet the needs of practical applications.

Key words: fraud phone identification, graph, multi-layer perceptrons, convolutional neural network, multi channel attention mechanism