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Full Description
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.
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 five-part, 12-chapter framework. It begins with foundational concepts and then dissects real-world fraud tactics. Part three 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 section 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.
Contents
1. Introduction 2. Traffic Fraud Tactics and Their Impact 3. Traffic Data Governance and Feature Engineering 4. Device Fingerprinting Technology 5. CAPTCHA Verification 6. Rules Engine 7. Countermeasures Against Machine Learning 8. Complex Network Adversarial Solutions 9. Multimodal Integrated Adversarial Solutions 10. New Adversarial Approaches 11. Operational System 12. Knowledge and Intelligence Mining and Applications
