Arul Kumar Natarajan - Böcker

This book provides a structured, practical exploration of modern computer networking concepts and the transformative role of artificial intelligence in cybersecurity. This book begins by introducing the foundational principles of computer networks, networking models, protocols, and architectures, together with essential concepts of artificial intelligence. Building on these fundamentals, it examines the evolving cyber threat landscape and explains how AI-driven techniques enhance threat detection, prevention, and response against contemporary attacks, such as phishing, ransomware, and zero-day exploits. It also covers secure networking practices, containerization, automated configuration management, and the role of automation in building resilient infrastructures.Subsequent chapters focus on advanced and applied topics, including Infrastructure as Code, DevSecOps integration, ethical hacking, penetration testing, incident response, and AI-assisted threat hunting. Emerging areas such as zero-trust networking, IoT security, large-scale enterprise deployments, and future trends in AI-powered cybersecurity are also discussed. Emphasis is placed on hands-on learning through labs, case studies, and real-world scenarios, supported by an accompanying GitHub repository.Designed for students and educators, this book bridges theoretical understanding with practical skills required for modern, AI-driven cyber defense environments. Professionals, network administrators, and cybersecurity practitioners interested in AI-driven security and modern network will also find this book useful as a reference.

Generative AI Revolution: Transforming Healthcare and Life Sciences towards Sustainable Development in Industry 6.0 examines how generative artificial intelligence is redefining modern healthcare systems. As the world moves toward Industry 6.0—an era of human-centric, intelligent, and sustainable innovation—this book explores how AI enhances patient care, medical diagnostics, drug discovery, and population health management. Organized into three thematic sections, the volume covers AI-driven patient engagement, mental health analytics, disease prediction models, deep learning–based imaging diagnostics, and AI-enabled drug development pipelines. It also addresses healthcare supply chains, epidemiological modeling, and data-driven decision support systems that strengthen resilience and efficiency in healthcare ecosystems. Emphasizing ethical and responsible AI adoption aligned with global sustainability goals, this book is an essential resource for researchers, healthcare professionals, policymakers, and industry leaders seeking to harness generative AI to build smarter, more sustainable healthcare solutions.