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

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 fra...

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Bibliographic Details
Published in2018 IEEE 4th International Conference on Computer and Communications (ICCC) pp. 1360 - 1365
Main Authors Zhang, Xin, Liu, Xuejun, Guo, Han
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.12.2018
Subjects
Advertising
Artificial Bee Colony
Back Propagation Neural Network
Backpropagation
Biological neural networks
click fraud
cost-sensitive
Feature extraction
feature selection
Neurons
Optimization
Training
Online AccessGet full text
DOI10.1109/CompComm.2018.8780941

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Summary: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.
DOI:10.1109/CompComm.2018.8780941