iBGP: A Bipartite Graph Propagation Approach for Mobile Advertising Fraud Detection
Joint Authors
Dong, Shoubin
Hu, Jinlong
Liang, Junjie
Source
Issue
Vol. 2017, Issue 2017 (31 Dec. 2017), pp.1-12, 12 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2017-04-03
Country of Publication
Egypt
No. of Pages
12
Main Subjects
Telecommunications Engineering
Abstract EN
Online mobile advertising plays a vital financial role in supporting free mobile apps, but detecting malicious apps publishers who generate fraudulent actions on the advertisements hosted on their apps is difficult, since fraudulent traffic often mimics behaviors of legitimate users and evolves rapidly.
In this paper, we propose a novel bipartite graph-based propagation approach, iBGP, for mobile apps advertising fraud detection in large advertising system.
We exploit the characteristics of mobile advertising user’s behavior and identify two persistent patterns: power law distribution and pertinence and propose an automatic initial score learning algorithm to formulate both concepts to learn the initial scores of non-seed nodes.
We propose a weighted graph propagation algorithm to propagate the scores of all nodes in the user-app bipartite graphs until convergence.
To extend our approach for large-scale settings, we decompose the objective function of the initial score learning model into separate one-dimensional problems and parallelize the whole approach on an Apache Spark cluster.
iBGP was applied on a large synthetic dataset and a large real-world mobile advertising dataset; experiment results demonstrate that iBGP significantly outperforms other popular graph-based propagation methods.
American Psychological Association (APA)
Hu, Jinlong& Liang, Junjie& Dong, Shoubin. 2017. iBGP: A Bipartite Graph Propagation Approach for Mobile Advertising Fraud Detection. Mobile Information Systems،Vol. 2017, no. 2017, pp.1-12.
https://search.emarefa.net/detail/BIM-1189131
Modern Language Association (MLA)
Hu, Jinlong…[et al.]. iBGP: A Bipartite Graph Propagation Approach for Mobile Advertising Fraud Detection. Mobile Information Systems No. 2017 (2017), pp.1-12.
https://search.emarefa.net/detail/BIM-1189131
American Medical Association (AMA)
Hu, Jinlong& Liang, Junjie& Dong, Shoubin. iBGP: A Bipartite Graph Propagation Approach for Mobile Advertising Fraud Detection. Mobile Information Systems. 2017. Vol. 2017, no. 2017, pp.1-12.
https://search.emarefa.net/detail/BIM-1189131
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1189131