Detection of Advertisement Click Fraud Using Machine Learning
Digital Advertising has taken long strides in generating revenues in modern world. The publishers and advertisers have made huge amount of money in this domain. Attackers make use of “click fraud” to generate money illegally. The repeated clicking on a particular ad, not out of interest in it but solely to earn money. To avoid such fraudulent activity, a detection model has been proposed to differentiate between legal and illegal users. This has been implemented using XGBoost algorithm which is more efficient and provides accurate results compared to other classification algorithms.
Keywords: Digital Advertisement, click fraud, XGBoost, classification algorithm, decision tree.