Detecting click fraud in online advertising: A data mining approach

Author

Richard OENTARYO, Singapore Management UniversityFollow
Ee Peng LIM, Singapore Management UniversityFollow
Michael FINEGOLD, Carnegie Mellon University
David LO, Singapore Management UniversityFollow
Feida ZHU, Singapore Management UniversityFollow
Clifton PHUA, Institute for Infocomm Research
Eng-Yeow CHEU, Institute for Infocomm Research
Ghim-Eng YAP, Institute for Infocomm Research
Kelvin SIM, Institute for Infocomm Research
Kasun PERERA, Masdar Institute of Science and Technology
Bijay NEUPANE, Masdar Institute of Science and Technology
Mustafa FAISAL, Masdar Institute of Science and Technology
Zeyar AUNG, Masdar Institute of Science and Technology
Wei Lee WOON, Masdar Institute of Science and Technology
Wei CHEN, Institute for InfoComm Research
Dhaval PATEL, Indian Institute of Technology Roorkee
Daniel BERRAR, Tokyo Institute of Technology

Publication Type

Journal Article

Version

acceptedVersion

Publication Date

1-2014

Abstract

Click fraud - the deliberate clicking on advertisements with no real interest on the product or service offered - is one of the most daunting problems in online advertising. Building an elective fraud detection method is thus pivotal for online advertising businesses. We organized a Fraud Detection in Mobile Advertising (FDMA) 2012 Competition, opening the opportunity for participants to work on real-world fraud data from BuzzCity Pte. Ltd., a global mobile advertising company based in Singapore. In particular, the task is to identify fraudulent publishers who generate illegitimate clicks, and distinguish them from normal publishers. The competition was held from September 1 to September 30, 2012, attracting 127 teams from more than 15 countries. The mobile advertising data are unique and complex, involving heterogeneous information, noisy patterns with missing values, and highly imbalanced class distribution. The competition results provide a comprehensive study on the usability of data mining-based fraud detection approaches in practical setting. Our principal findings are that features derived from fine-grained time series analysis are crucial for accurate fraud detection, and that ensemble methods offer promising solutions to highly-imbalanced nonlinear classification tasks with mixed variable types and noisy/missing patterns.

Keywords

Data mining, Ensemble learning, Feature engineering, Fraud detection, Imbalanced classification

Discipline

Advertising and Promotion Management | Databases and Information Systems | Information Security | Numerical Analysis and Scientific Computing

Research Areas

Data Science and Engineering

Publication

Journal of Machine Learning Research

Volume

15

Issue

1

First Page

99

Last Page

140

ISSN

1533-7928

Publisher

MIT Press

Citation

OENTARYO, Richard; LIM, Ee Peng; FINEGOLD, Michael; LO, David; ZHU, Feida; PHUA, Clifton; CHEU, Eng-Yeow; YAP, Ghim-Eng; SIM, Kelvin; PERERA, Kasun; NEUPANE, Bijay; FAISAL, Mustafa; AUNG, Zeyar; WOON, Wei Lee; CHEN, Wei; PATEL, Dhaval; and BERRAR, Daniel. Detecting click fraud in online advertising: A data mining approach. (2014). Journal of Machine Learning Research. 15, (1), 99-140.
Available at: https://ink.library.smu.edu.sg/sis_research/1990

Copyright Owner and License

Authors

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.

Comments

Submit request for dataset at https://larc.smu.edu.sg/buzzcity-mobile-advertisement-dataset

Additional URL

https://www.jmlr.org/papers/volume15/oentaryo14a/oentaryo14a.pdf