论文标题
黑市帐户检测的自我监督图表学习
Self-supervised Graph Representation Learning for Black Market Account Detection
论文作者
论文摘要
如今，多用途消息传递移动应用程序（MMMA）变得越来越普遍。 MMMA吸引了欺诈者，一些网络犯罪分子通过黑市帐户（BMA）为欺诈提供了支持。与欺诈者相比，BMA不直接参与欺诈，并且更难发现。本文说明了我们在微信中使用的BMA检测系统SGRL（自我监督的图表学习），这是代表性MMMA，具有超过十亿个用户。我们在SGRL中量身定制图形神经网络和图形自我监督学习，以进行BMA检测。 SGRL的工作流程包含一个预处理的阶段，该阶段利用结构信息，节点属性信息和可用的人类知识以及轻巧的检测阶段。在离线实验中，在离线评估措施中，SGRL的表现优于最先进的方法的最先进方法为16.06％-58.17％。我们在在线环境中部署SGRL，以检测数十亿微信图上的BMA，并且在在线评估措施中，它超过了替代方案的7.27％。总之，SGRL可以减轻标签的依赖，很好地概括到看不见的数据，并有效地检测到微信中的BMA。
Nowadays, Multi-purpose Messaging Mobile App (MMMA) has become increasingly prevalent. MMMAs attract fraudsters and some cybercriminals provide support for frauds via black market accounts (BMAs). Compared to fraudsters, BMAs are not directly involved in frauds and are more difficult to detect. This paper illustrates our BMA detection system SGRL (Self-supervised Graph Representation Learning) used in WeChat, a representative MMMA with over a billion users. We tailor Graph Neural Network and Graph Self-supervised Learning in SGRL for BMA detection. The workflow of SGRL contains a pretraining phase that utilizes structural information, node attribute information and available human knowledge, and a lightweight detection phase. In offline experiments, SGRL outperforms state-of-the-art methods by 16.06%-58.17% on offline evaluation measures. We deploy SGRL in the online environment to detect BMAs on the billion-scale WeChat graph, and it exceeds the alternative by 7.27% on the online evaluation measure. In conclusion, SGRL can alleviate label reliance, generalize well to unseen data, and effectively detect BMAs in WeChat.
