Book

Description

As roads grow smarter and vehicles become increasingly connected, the security of Vehicular Ad Hoc Networks has never been more critical — or more complex. AI Techniques for VANET Security offers a rigorous and comprehensive examination of how Artificial Intelligence is reshaping the way we protect intelligent transportation systems. From the dynamic topology of vehicle-to-vehicle communication to the sophisticated threat landscapes targeting modern road infrastructure, this book confronts the real challenges of securing VANETs head-on. Readers are taken on a structured journey through the full spectrum of AI-driven security solutions: Machine Learning for real-time anomaly detection, Deep Learning for uncovering complex spatial and temporal attack patterns, Reinforcement Learning for adaptive and self-improving defenses, and Federated Learning for privacy-preserving collaboration across distributed vehicles. Trust management systems — the backbone of node reliability in decentralized networks — are explored in depth, bridging mathematical models with cutting-edge AI methods. Each chapter combines theoretical foundations with mathematical formulations, algorithmic frameworks, and real-world case studies, making this an essential reference for researchers, engineers, and graduate students working at the intersection of cybersecurity, AI, and intelligent transportation. Whether you are designing the next generation of connected vehicle systems or building the security protocols that will protect them, this book equips you with the knowledge to meet the challenge.