Online mobile advertising plays a vital role in the mobile app ecosystem. The mobile advertising frauds caused by fraudulent clicks or other actions on advertisements are considered one of the most critical issues in mobile advertising systems. To combat the evolving mobile advertising frauds, machine learning methods have been successfully applied to identify advertising frauds in tabular data, distinguishing suspicious advertising fraud operation from normal one. However, such approaches may suffer from labor-intensive feature engineering and robustness of the detection algorithms, since the online advertising big data and complex fraudulent advertising actions generated by malicious codes, botnets, and click-firms are constantly changing. In this paper, we propose a novel weighted heterogeneous graph embedding and deep learning-based fraud detection approach, namely, GFD, to identify fraudulent apps for mobile advertising. In the proposed GFD approach, (i) we construct a weighted heterogeneous graph to represent behavior patterns between users, mobile apps, and mobile ads and design a weighted metapath to vector algorithm to learn node representations (graph-based features) from the graph; (ii) we use a time window based statistical analysis method to extract intrinsic features (attribute-based features) from the tabular sample data; (iii) we propose a hybrid neural network to fuse graph-based features and attribute-based features for classifying the fraudulent apps from normal apps. The GFD approach was applied on a large real-world mobile advertising dataset, and experiment results demonstrate that the approach significantly outperforms well-known learning methods.

中文翻译：

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GFD：基于加权异构图嵌入的移动广告欺诈检测方法

在线移动广告在移动应用生态系统中起着至关重要的作用。由广告上的欺诈性点击或其他动作引起的移动广告欺诈被认为是移动广告系统中最关键的问题之一。为了应对不断发展的移动广告欺诈行为，机器学习方法已成功应用于识别表格数据中的广告欺诈行为，从而将可疑的广告欺诈行为与正常行为区分开。然而，由于在线广告大数据和由恶意代码，僵尸网络和点击确认产生的复杂的欺诈性广告动作不断变化，因此这些方法可能会遭受劳动强度大的特征工程和检测算法的鲁棒性的困扰。在本文中，我们提出了一种新颖的加权异构图嵌入和基于深度学习的欺诈检测方法GFD，以识别用于移动广告的欺诈应用。在提出的GFD方法中，（i）我们构造了一个加权异构图来表示用户，移动应用程序和移动广告之间的行为模式，并设计了一个加权元路径到矢量算法来从图中学习节点表示（基于图的特征）；（ii）我们使用基于时间窗口的统计分析方法从表格样本数据中提取内在特征（基于属性的特征）；（iii）我们提出了一种混合神经网络，以融合基于图的特征和基于属性的特征，以将欺诈性应用与普通应用进行分类。GFD方法已应用于大型现实移动广告数据集，