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

研究成果: Article

21 引用 (Scopus)

抜粋

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/.

元の言語English
ページ(範囲)99-140
ページ数42
ジャーナルJournal of Machine Learning Research
15
出版物ステータスPublished - 2014
外部発表Yes

    フィンガープリント

ASJC Scopus subject areas

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

これを引用

Oentaryo, R., Lim, E. P., Finegold, M., Lo, D., Zhu, F., Phua, C., ... Berrar, D. (2014). Detecting click fraud in online advertising: A data mining approach. Journal of Machine Learning Research, 15, 99-140.