CLICK FRAUD DETECTION IN ONLINE ADVERTISING USING ADVANCED MACHINE LEARNING MODELS

Abstract

The rapid growth of online advertising has significantly increased the prevalence of ad click fraud, where malicious entities generate illegitimate clicks on digital advertisements to manipulate revenue models and exhaust advertising budgets. Click fraud poses a serious threat to advertisers, publishers, and advertising platforms by distorting analytics, reducing return on investment, and undermining trust in digital ecosystems. Traditional rule-based detection systems are often ineffective in identifying sophisticated fraud patterns due to their inability to adapt to evolving attack strategies. This paper proposes a robust framework for ad click fraud detection using machine learning and deep learning algorithms to accurately distinguish between legitimate and fraudulent clicks. The proposed system integrates data preprocessing, feature engineering, and hybrid learning models, including decision trees, random forests, support vector machines, and deep neural networks, to analyze clickstream data. Key features such as click frequency, session duration, IP behavior, device information, and temporal patterns are utilized to capture anomalies associated with fraudulent activities. The framework employs ensemble techniques to enhance prediction accuracy and reduce false positives, while deep learning models capture complex nonlinear relationships within the data. Experimental results demonstrate that the proposed approach significantly outperforms traditional methods in terms of accuracy, precision, recall, and F1-score. Additionally, the system exhibits strong scalability and adaptability to large-scale advertising environments. This research contributes to the development of intelligent and automated fraud detection systems that can effectively mitigate click fraud and improve the reliability of online advertising platforms.