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 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. |Big Data Security Governance and Prevention Traffic Anti-Fraud in Practice