A hybrid and effective learning approach for Click Fraud detection

Abstract

Click Fraud is a fraudulent act of clicking on pay-per-click advertisements to increase the site’s revenue or to drain revenue from the advertiser. This illegal act has been putting commercial industries in a dilemma for quite some time. These industries think twice before advertising their products on websites and mobile-apps, as many parties try to exploit them. To safely promote their products, there must be an efficient system to detect click fraud. To address this problem, we propose a model called CFXGB (Cascaded Forest and XGBoost). The proposed model, classified under supervised machine learning, is a combination of two learning models used for feature transformation and classification. We showcase its superior performance compared to other related models, and make a comparison with multiple click fraud datasets with varying sizes.

Keywords

• Click Fraud
• Ad network
• Cascaded Forest
• Feature transformation
• XGBoost