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Detecting Coalition Attacks in Online Advertising: A hybrid data mining approach

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

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.
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Keywords Data mining; fraud detection; internet publisher; impression; MapReduce; online advertising

Citation: Qinglei Zhang, Wenying Feng. Detecting Coalition Attacks in Online Advertising: A hybrid data mining approach. Big Data and Information Analytics, 2016, 1(2): 227-245. doi: 10.3934/bdia.2016006


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Copyright Info: 2016, Wenying Feng, et al., licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution Licese (http://creativecommons.org/licenses/by/4.0)

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