论文标题
恶意帐户检测的异质图神经网络
Heterogeneous Graph Neural Networks for Malicious Account Detection
论文作者
论文摘要
我们介绍了Gem，这是第一种在世界领先的无移动现金支付平台之一Alipay上检测恶意帐户的异质图神经网络方法。我们的方法是从连接的子图方法启发的，自适应地学习了基于攻击者的两个基本弱点的异质帐户设备图的歧视性嵌入，即设备聚集和活动聚合。对于异质图由各种类型的节点组成，我们提出了一种注意机制，以了解不同类型的节点的重要性，同时使用总和运算符对每种类型中节点的聚合模式进行建模。实验表明，与随着时间的推移，与竞争方法相比，我们的方法始终如一地执行有希望的结果。
We present, GEM, the first heterogeneous graph neural network approach for detecting malicious accounts at Alipay, one of the world's leading mobile cashless payment platform. Our approach, inspired from a connected subgraph approach, adaptively learns discriminative embeddings from heterogeneous account-device graphs based on two fundamental weaknesses of attackers, i.e. device aggregation and activity aggregation. For the heterogeneous graph consists of various types of nodes, we propose an attention mechanism to learn the importance of different types of nodes, while using the sum operator for modeling the aggregation patterns of nodes in each type. Experiments show that our approaches consistently perform promising results compared with competitive methods over time.
