计算机科学
密码
代表(政治)
关系(数据库)
嵌入
背景(考古学)
人工神经网络
钥匙(锁)
计算机安全
相似性(几何)
图形
特征(语言学)
理论计算机科学
人工智能
数据挖掘
图像(数学)
古生物学
语言学
哲学
政治
生物
政治学
法学
标识
DOI:10.1109/smartcomp55677.2022.00052
摘要
Account takeover is a type of malicious attack where a fraudster steals accounts and passwords from normal users, causing the loss of money and the exposure of personal information. Existing solutions either rely on extensive manual labeling, or require behavior sequences and context graphs of accounts. In this paper, I propose a Siamese neural Network-based Multi-Relation Graph Embedding method (MRGE-SiameseNet) to detect stolen accounts. The key idea of MRGE-SiameseNet is that the two inputs from the same users are similar. I adopt the idea of the siamese neural network to judge whether two different inputs are from the same user. To get a powerful representation of each account, I integrate several embeddings of multiple relationships of accounts and profile feature embedding for each account with multi-head attention mechanism. The fully connected module is employed to obtain the similarity score, which can be utilized to identify whether the account is stolen by account takeover.
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