GFD: A Weighted Heterogeneous Graph Embedding Based Approach for Fraud Detection in Mobile Advertising

المؤلفون المشاركون

Dong, Shoubin
Li, Tenghui
Huang, Song
Hu, Jinlong
Zhuang, Yi

المصدر

Security and Communication Networks

العدد

المجلد 2020، العدد 2020 (31 ديسمبر/كانون الأول 2020)، ص ص. 1-12، 12ص.

الناشر

Hindawi Publishing Corporation

تاريخ النشر

2020-09-04

دولة النشر

مصر

عدد الصفحات

12

التخصصات الرئيسية

تكنولوجيا المعلومات وعلم الحاسوب

الملخص EN

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.

نمط استشهاد جمعية علماء النفس الأمريكية (APA)

Hu, Jinlong& Li, Tenghui& Zhuang, Yi& Huang, Song& Dong, Shoubin. 2020. GFD: A Weighted Heterogeneous Graph Embedding Based Approach for Fraud Detection in Mobile Advertising. Security and Communication Networks،Vol. 2020, no. 2020, pp.1-12.
https://search.emarefa.net/detail/BIM-1208580

نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)

Hu, Jinlong…[et al.]. GFD: A Weighted Heterogeneous Graph Embedding Based Approach for Fraud Detection in Mobile Advertising. Security and Communication Networks No. 2020 (2020), pp.1-12.
https://search.emarefa.net/detail/BIM-1208580

نمط استشهاد الجمعية الطبية الأمريكية (AMA)

Hu, Jinlong& Li, Tenghui& Zhuang, Yi& Huang, Song& Dong, Shoubin. GFD: A Weighted Heterogeneous Graph Embedding Based Approach for Fraud Detection in Mobile Advertising. Security and Communication Networks. 2020. Vol. 2020, no. 2020, pp.1-12.
https://search.emarefa.net/detail/BIM-1208580

نوع البيانات

مقالات

لغة النص

الإنجليزية

الملاحظات

Includes bibliographical references

رقم السجل

BIM-1208580