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...

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Main Authors: 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
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Language:English
Published: Institutional Knowledge at Singapore Management University 2014
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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
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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
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