International Journal on Science and Technology

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Advanced Machine Learning Techniques for Fraud Detection in Programmatic Advertising

Author(s) Siddharth Gupta
Country United States
Abstract This comprehensive article explores the evolution and implementation of advanced machine learning techniques in fraud detection within programmatic advertising. The article examines various approaches, including supervised, unsupervised, and deep learning methods, highlighting their effectiveness in
combating sophisticated fraud patterns. The article analyzes infrastructure requirements, performance optimization strategies, and the integration of real-time analytics while addressing privacy and compliance considerations. The investigation encompasses system architecture components, scaling mechanisms, and monitoring protocols essential for maintaining optimal performance in high-volume environments. Furthermore, the article evaluates emerging technologies such as federated learning and reinforcement learning, demonstrating their impact on improving detection capabilities and
cross-organizational collaboration.
Keywords Machine Learning Fraud Detection, Programmatic Advertising Security, Real-time Analytics, Privacy-Preserving Computing, Advanced Infrastructure Optimization
Field Computer
Published In Volume 16, Issue 1, January-March 2025
Published On 2025-03-15
Cite This Advanced Machine Learning Techniques for Fraud Detection in Programmatic Advertising - Siddharth Gupta - IJSAT Volume 16, Issue 1, January-March 2025. DOI 10.71097/IJSAT.v16.i1.2416
DOI https://doi.org/10.71097/IJSAT.v16.i1.2416
Short DOI https://doi.org/g88sb4
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