Unleashing Deepnets to Combat ad Click Fraud and Safeguarding Online Advertising

As online advertising continues to be a significant revenue source for businesses, the threat of ad click fraud poses a substantial challenge to the legitimacy and effectiveness of these campaigns. Adversarial actors engage in ad click fraud to generate false clicks on ads, leading to financial losses for advertisers and misleading performance metrics. This paper proposes a novel approach to combating ad click fraud using Deep Neural Networks (Deepnets). By leveraging the power of Deepnets, which excel at pattern recognition and anomaly detection, advertisers can develop robust fraud detection systems that identify and mitigate fraudulent click activities. The proposed method utilizes historical click data to train Deepnets, enabling them to learn the complex relationships between various attributes associated with genuine and fraudulent clicks. Through extensive experimentation, the research demonstrate the effectiveness of Deepnets in accurately distinguishing between legitimate and fraudulent clicks, thus safeguarding online advertising budgets and enhancing campaign outcomes. This research contributes to the practical application of deep learning in the field of online advertising security and establishes a foundation for further advancements in fraud detection techniques.