Big Data Security Governance and Prevention: Traffic Anti Fraud in Practice

This book provides a practical reference for traffic anti-fraud, establishing a new standard for accessible, real-world traffic security governance that empowers readers to design scalable defenses while maintaining optimal user experience. This book provides a practical reference for traffic anti-fraud, establishing a new standard for accessible, real-world traffic security governance that empowers readers to design scalable defenses while maintaining an optimal user experience. The internet’s rapid growth has enabled a surge in digital fraud. Cybercriminals exploit every stage of online traffic, from fake promotion scams and bot-driven account fraud to "coupon hacking" during e-commerce sales and sophisticated phishing campaigns. These threats cost billions globally and demand urgent solutions to protect users and platforms. This practical guide demystifies traffic anti-fraud with a 12-chapter framework. It begins with foundational concepts and then dissects real-world fraud tactics, then focuses on data preparation and governance. Core chapters introduce cutting-edge tools, such as device fingerprinting, AI-powered anomaly detection, graph-based network analysis, and cross-modal threat fusion. The final chapter provides step-by-step strategies for building adaptive anti-fraud systems. This exceptional resource is ideal for cybersecurity professionals, developers, researchers, and students interested in cybercrime prevention, risk governance, and big data security.