Detecting click fraud in online advertising: A data mining approach

Richard Oentaryo, Ee Peng Lim, Michael Finegold, David Lo, Feida Zhu, Clifton Phua, Eng Yeow Cheu, Ghim Eng Yap, Kelvin Sim, Minh Nhut Nguyen, Kasun Perera, Bijay Neupane, Mustafa Faisal, Zeyar Aung, Wei Lee Woon, Wei Chen, Dhaval Patel, Daniel Berrar

Research output: Contribution to journalArticle

21 Citations (Scopus)

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 effective 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 timeseries 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. The competition data remain available for further studies at http://palanteer.sis.smu.edu.sg/fdma2012/.

Original languageEnglish
Pages (from-to)99-140
Number of pages42
JournalJournal of Machine Learning Research
Volume15
Publication statusPublished - 2014 Jan

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Keywords

  • Ensemble learning
  • Feature engineering
  • Fraud detection
  • Imbalanced classification

ASJC Scopus subject areas

  • Software
  • Control and Systems Engineering
  • Statistics and Probability
  • Artificial Intelligence

Cite this

Oentaryo, R., Lim, E. P., Finegold, M., Lo, D., Zhu, F., Phua, C., Cheu, E. Y., Yap, G. E., Sim, K., Nguyen, M. N., Perera, K., Neupane, B., Faisal, M., Aung, Z., Woon, W. L., Chen, W., Patel, D., & Berrar, D. (2014). Detecting click fraud in online advertising: A data mining approach. Journal of Machine Learning Research, 15, 99-140.