海报:基于神经网络的图嵌入恶意账户检测

Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security Pub Date : 2017-10-30 DOI:10.1145/3133956.3138827

Ziqi Liu, Chaochao Chen, Jun Zhou, Xiaolong Li, Feng Xu, Tao Chen, Le Song

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引用次数: 4

摘要

我们提出了一种基于神经网络的图嵌入方法来检测支付宝的恶意账户，支付宝是世界领先的移动支付平台之一。我们的方法基于攻击者的两个基本弱点，即设备聚合和活动聚合，自适应地从帐户-设备图中学习判别嵌入。实验表明，与现有方法相比，我们的方法获得了更好的查准率曲线。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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POSTER: Neural Network-based Graph Embedding for Malicious Accounts Detection

We present a neural network based graph embedding method for detecting malicious accounts at Alipay, one of the world's leading mobile payment platform. Our method adaptively learns discriminative embeddings from an account-device graph based on two fundamental weaknesses of attackers, i.e. device aggregation and activity aggregation. Experiments show that our method achieves outstanding precision-recall curve compared with existing methods.

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来源期刊

Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security

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