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A Click Fraud Detection Scheme based on Cost sensitive BPNN and ABC in Mobile Advertising | IEEE Conference Publication | IEEE Xplore

A Click Fraud Detection Scheme based on Cost sensitive BPNN and ABC in Mobile Advertising


Abstract:

Click fraud happens in cost per click ad networks where publishers charge advertisers for each click. Click fraud is posing the huge loss to the mobile advertising indust...Show More

Abstract:

Click fraud happens in cost per click ad networks where publishers charge advertisers for each click. Click fraud is posing the huge loss to the mobile advertising industry. The conventional technologies use ensemble machine learning methods, neglecting the cost of incorrect classification for a fraud publisher is higher than a normal publisher. An effective classification model for variable click fraud is proposed in this paper. Cost-sensitive Back Propagation Neural Network is combined with the novel Artificial Bee Colony algorithm in this research (CSBPNN-ABC). Feature selection is synchronously optimized with BPNN connection weights by ABC to reduce the interaction between features and weights. Cost Parameters are added to BPNN by correcting the error function. Experiments on real world click data in mobile advertising show that its superior classification performance compared with the state-of-the-art technology.
Date of Conference: 07-10 December 2018
Date Added to IEEE Xplore: 01 August 2019
ISBN Information:
Conference Location: Chengdu, China

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