The Effect of Feature Reduction in Click Fraud Detection: Review

Abstract

It is almost impossible for online activities being without fraud. Online ads face a major threat represents by fake clicks which happen because of bots or some mischievous people. Several studies have solved the problem using machine learning algorithms. Some of them have solved only the problem of automatic click fraud (which carried out using bot), to classify physical or bot click. While many recent researches have detected click fraud problem in spite of clicks type. This paper presents a survey of methods used to detect fraud clicks on ads. It presents advantages, as well as disadvantages of each method, in general, Most recent studies in this field, have focused on features preprocessing before classification, because of the problems’ type which imposed existence many related features and this may lead to overfitting. So the solution is applying dimensional reduction algorithms, to get better results and avoid overfitting.

Keywords: Click Fraud, dimensional reduction, features, Online advertising, pay_per_click.

DOI: 10.7176/NCS/11-01

Publication date:July 31st 2020