Neural Network-Based Deep Learning for Online Payment Fraud Detection

Inbunden, Engelska, 2026

1 334 kr

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Beskrivning

This book explores deep learning as a next-generation approach to online payment fraud detection in the face of increasingly complex and adaptive threats. Traditional rule-based or shallow learning methods are no longer sufficient. Through ten focused chapters, this book tackles challenges such as behavioral modeling, spatiotemporal anomaly detection, class imbalance, behavior drift, and graph-based inference. It applies advanced neural architectures including LSTM, GRU, GANs, GNNs, and spatiotemporal transformers. With a problem-driven structure, each chapter links real-world fraud problems to tailored neural solutions, validated on large-scale transaction data. This book blends theory, practical design, and empirical rigor, offering researchers and practitioners a foundation for scalable, adaptive, and reliable fraud detection systems.

Produktinformation

Utgivningsdatum:2026-05-16
Mått:155 x 235 x 17 mm
Vikt:469 g
Format:Inbunden
Språk:Engelska
Antal sidor:185
Förlag:Springer Verlag, Singapore
ISBN:9789819585120

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Mer om författaren

1. Yu XieYu Xie received the B.S. degree in information security from Qingdao University in 2017 and the Ph.D. degree in computer software and theory from Tongji University in 2022. He is now a lecturer at the College of Information Engineering, Shanghai Maritime University. With a solid academic background in data science and cybersecurity, his recent research has focused on the application of machine learning and deep learning techniques in financial transaction systems. He has contributed to several projects involving online payment security, real-time fraud detection, and risk analysis. His experience in both academic research and practical implementations positions him well to address the technical and theoretical aspects of fraud detection using neural networks.2. Yue TianYue Tian received the Ph.D. degree in computer science and technology from Tongji University, Shanghai, China, in 2025. She is currently a lecturer with the Department of Computer Science and Technology, Shanghai Normal University. Her research interests include credit card fraud detection, graph neural networks, machine learning, and Explainable AI.3. Jiamin YaoJiamin Yao received the B.S. degree in software engineering from the China University of Petroleum, Qingdao, China, in 2017, the M.S. degree in software engineering from China University of Petroleum, Qingdao, China, in 2020, and the Ph.D. degree in computer software and theory from Tongji University, Shanghai, China, in 2024. She is now a lecturer at the College of Information Engineering, Shanghai Maritime University. Her primary research interests lie in software-defined networks (SDN), resource management, and deep reinforcement learning. She focuses on designing intelligent, adaptive systems, and optimization algorithms for complex and dynamic environments.4. Guanjun LiuGuanjun Liu received the Ph.D. degree in Computer Software and Theory from Tongji University, China, in 2011. Dr. Liu was a Post-doctoral Research Fellow with the Singapore University of Technology and Design, Singapore, from 2011 to 2013, and a Post-doctoral Research Fellow with the Humboldt University of Berlin, Germany, from 2013 to 2014, supported by the Alexander von Humboldt Foundation. He is a professor at the School of Computer Science and Technology, Tongji University. He has authored 5 books and more than 160 papers. His research interests include trustworthy computing and trustworthy artificial intelligence.

Innehållsförteckning

Introduction.- Foundations of Online Fraud Detection and Deep Learning Models.- Learning Fraud Sensitive Transactional Representations via Attention and Temporal Modeling.- Extending Behavioral Modeling with Spatial Temporal Learning.- Addressing Class Imbalance through Time-Aware Generative Sample Enrichment.- Reducing Behavioral Overlap via Hybrid Sampling and Distribution Refinement.- Hierarchical Gated Networks for Deep Transactional Feature Learning.- Capturing Transactional Drift via Current Historical Behavior Interaction.- Graph Neural Network for Online Payment Fraud Detection.- Spatial-Temporal-Aware Graph Transformer for Online Payment Fraud Detection.