Detecting click fraud in online advertising: A data mining approach
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...
Saved in:
Main Authors: | , , , , , , , , , , , , , , , , |
---|---|
Format: | text |
Language: | English |
Published: |
Institutional Knowledge at Singapore Management University
2014
|
Subjects: | |
Online Access: | https://ink.library.smu.edu.sg/sis_research/1990 https://ink.library.smu.edu.sg/context/sis_research/article/2989/viewcontent/Detecting_Click_Fraud_in_Online_Advertising__A_Data_Mining_Approah2014.pdf |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Institution: | Singapore Management University |
Language: | English |
id |
sg-smu-ink.sis_research-2989 |
---|---|
record_format |
dspace |
spelling |
sg-smu-ink.sis_research-29892021-03-12T08:01:24Z Detecting click fraud in online advertising: A data mining approach 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 BERRAR, Daniel 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. 2014-01-01T08:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/1990 https://ink.library.smu.edu.sg/context/sis_research/article/2989/viewcontent/Detecting_Click_Fraud_in_Online_Advertising__A_Data_Mining_Approah2014.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University Data mining Ensemble learning Feature engineering Fraud detection Imbalanced classification Advertising and Promotion Management Databases and Information Systems Information Security Numerical Analysis and Scientific Computing |
institution |
Singapore Management University |
building |
SMU Libraries |
continent |
Asia |
country |
Singapore Singapore |
content_provider |
SMU Libraries |
collection |
InK@SMU |
language |
English |
topic |
Data mining Ensemble learning Feature engineering Fraud detection Imbalanced classification Advertising and Promotion Management Databases and Information Systems Information Security Numerical Analysis and Scientific Computing |
spellingShingle |
Data mining Ensemble learning Feature engineering Fraud detection Imbalanced classification Advertising and Promotion Management Databases and Information Systems Information Security Numerical Analysis and Scientific Computing 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 BERRAR, Daniel Detecting click fraud in online advertising: A data mining approach |
description |
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. |
format |
text |
author |
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 BERRAR, Daniel |
author_facet |
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 BERRAR, Daniel |
author_sort |
OENTARYO, Richard |
title |
Detecting click fraud in online advertising: A data mining approach |
title_short |
Detecting click fraud in online advertising: A data mining approach |
title_full |
Detecting click fraud in online advertising: A data mining approach |
title_fullStr |
Detecting click fraud in online advertising: A data mining approach |
title_full_unstemmed |
Detecting click fraud in online advertising: A data mining approach |
title_sort |
detecting click fraud in online advertising: a data mining approach |
publisher |
Institutional Knowledge at Singapore Management University |
publishDate |
2014 |
url |
https://ink.library.smu.edu.sg/sis_research/1990 https://ink.library.smu.edu.sg/context/sis_research/article/2989/viewcontent/Detecting_Click_Fraud_in_Online_Advertising__A_Data_Mining_Approah2014.pdf |
_version_ |
1770571769880510464 |