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Advertisement Click Fraud Detection and Prevention: A machine learning approach
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| 2.06 MB | Adobe PDF |
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Orientador(es)
Resumo(s)
Click fraud poses a significant challenge to digital advertising, causing substantial financial
losses and undermining advertiser trust. The study explores the potential of machine learning
approaches for detecting such malicious conduct in Google Ads. We use five algorithms for
modelling and comparison, including support vector machines, random forest, k-nearest
neighbours, gradient tree boosting, and XGBoost. These are all part of the CRISP-DM
methodology, which gives you a structured way to do machine learning projects. These
models were chosen for their proven efficacy in fraud detection. Our analysis revealed that
tree-based models, particularly GTB and XGBoost, outperformed others in accuracy, recall,
and AUC, making them highly effective in identifying fraudulent clicks. The study confirms that
machine learning algorithms can accurately classify and detect fraudulent activities,
enhancing the understanding of fraud characteristics using pre-classified data. Additionally,
we identified key patterns and characteristics associated with non-genuine clicks, such as
primary click actions and click frequency per IP address and user ID. This research bridges the
gap between academic theory and practical application, providing actionable insights for
marketing agencies to combat click fraud effectively. A collaboration with a marketing agency
for this study ensures that the outcomes are directly beneficial, enhancing the overall integrity
and performance of digital advertising efforts.
Descrição
Dissertation presented as the partial requirement for obtaining a Master's degree in Data Driven Marketing, specialization in Data Science for Marketing
Palavras-chave
Click fraud machine learning advertising detection ads SDG 8 - Decent work and economic growth SDG 16 - Peace, justice and strong institutions