Detecting coalition attacks in online advertising: A hybrid data mining approach

Qinglei  Zhang; Wenying  Feng

doi:10.3934/bdia.2016006

Article Contents

2016, Volume 1, Issue 2&3: 227-245. Doi: 10.3934/bdia.2016006

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Detecting coalition attacks in online advertising: A hybrid data mining approach

Qinglei Zhang^1, and
Wenying Feng^1,

1.
Department of Computing & Information Systems, Department of Mathematics, Trent University, Peterborough, Ontario K9J 0G2

Received: January 31, 2016

Revised: August 31, 2016

Published: August 2016

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Abstract

Coalition attack is nowadays one of the most common type of attacks in the industry of online advertising. In this paper, we attempt to mitigate the problem of frauds by proposing a hybrid framework that detects the coalition attacks based on multiple metrics. We also articulate the theoretical basis for these metrics to be integrated into the hybrid framework. Furthermore, we instance the framework with two metrics and develop a detection system that identifies the coalition attacks from two distinguish perspectives.

Keywords:

Mathematics Subject Classification: Primary: 93E10, 93E35; Secondary: 62M20, 62H30.

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