부산대학교 도서관

Mobile advertising has become inarguably one of the fastest growing industries all over the world. The influx of capital attracts increasing fraudsters to defraud money from advertisers. There are many tricks a fraudster can leverage, among which bot install fraud is undoubtedly the most insidious one due to its ability to implement sophisticated behavioral patterns and emulate normal users, so as to evade from detection rules defined by human experts. In this work, we propose an anti-fraud method based on heterogeneous graph that incorporates both local context and global context via graph neural networks (GNN) and gradient boosting classifier to detect bot fraud installs at Mobvista, a leading global mobile advertising company. Offline evaluations in two datasets show the proposed method outperforms all the competitive baseline methods by at least 2.2% in the first dataset and 5.75% in the second dataset given the evaluation metric Recall@90% Precision. Furthermore, we deploy our method to tackle million-scale data daily at Mobvista. The online performance also shows that the proposed methods consistently detect more bots than other baseline methods.