Big Data Security Governance and Prevention

Traffic Anti-Fraud in Practice
1. Auflage 2026
ISBN: 978-1-041-25535-2
Verlag: Taylor & Francis Ltd

Buch, Englisch, 224 Seiten, Format (B × H): 178 mm x 254 mm

Reihe: Data Communication Series

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.

Zielgruppe

Academic, Postgraduate, Professional Practice & Development, Professional Reference, Undergraduate Advanced, and Undergraduate Core

Autoren/Hrsg.

Zhang, Kai

Hao, Liyang

Yang, Ze

Xiong, Qi

Fachgebiete

• Mathematik | Informatik EDV | Informatik Technische Informatik Computersicherheit Kryptographie, Datenverschlüsselung
• Mathematik | Informatik EDV | Informatik Computerkommunikation & -vernetzung Netzwerksicherheit

Inhaltsverzeichnis

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

Über Autor(innen)

Kai Zhang is Tencent Principal Engineer with over a decade of experience in combating cybercrimes. He has led security projects in game security protection, financial risk control systems, and anti-fraud architectures. His core expertise lies in big data security threat modeling.

Ze Yang is Tencent Researcher dedicated to financial risk governance. He has developed AI-powered mechanisms to combat underground economy threats in payment ecosystems.

Liyang Hao is Tencent Researcher focusing on behavioral security systems. He has designed real-time gambling/fraud intervention engines for social payment scenarios.

Qi Xiong is Tencent Principal Engineer with 15 years of security architecture experience. He has spearheaded compliance-driven security solutions for fintech applications and mobile ecosystems.