Generative AI for Cybersecurity and Privacy

Generative AI for Cybersecurity and Privacy offers a groundbreaking exploration of how generative artificial intelligence is reshaping the landscape of cybersecurity and privacy protection in an era of rapid digital transformation. As cyber threats grow in sophistication and scale, this book provides a timely and authoritative guide to harnessing generative AI to safeguard digital ecosystems, secure sensitive data, and address emerging challenges across diverse domains. Spanning a series of expertly curated chapters, this volume delves into cutting-edge advancements and practical applications of generative AI in cybersecurity. It covers critical areas such as AI-driven threat detection and response, privacy-preserving AI models, secure IoT and cloud computing frameworks, and robust defenses for cyber-physical systems, including Smart Cities and wireless networks. The book balances rigorous theoretical foundations with real-world case studies, making it an essential resource for researchers, security professionals, policymakers, and organizational leaders. The book offers comprehensive coverage of key topics, including: . Leveraging generative AI for proactive threat detection, risk analysis, and automated incident response . Innovative approaches to data privacy, compliance, and governance in AI-driven systems . Advanced methodologies for securing IoT, mobile applications, and cloud infrastructures . Practical frameworks for integrating generative AI into cybersecurity strategies for critical infrastructures . Emerging applications of generative AI in personalized, secure digital experiences, such as e-commerce and smart systems Authored by a global team of leading researchers and practitioners, this book stands out by not only addressing current cybersecurity and privacy challenges but also proposing forward-thinking, scalable solutions powered by generative AI. Unlike traditional resources, it emphasizes the transformative potential of AI in revolutionizing risk analysis, threat mitigation, and privacy preservation across multiple domains. Whether you're navigating the complexities of IoT, cloud security, or emerging cyber threats, Generative AI for Cybersecurity and Privacy equips you with the knowledge and tools to build intelligent, secure, and future-ready strategies for a dynamic digital world.

The authors of this book hold Ph.D degrees, and they have published many journal articles and book chapters. Dr.Yassine Maleh is the author of many published books with well-known publishers like CRC Press and Springer.

Part I: Foundations and Core Concepts 1. A Systematic Review of Machine Learning and Deep Learning Applications in Information Security 2. Impact of Generative AI on Cybersecurity and Data Privacy Part II: Applications in Threat Detection and Defense 3. Generative Adversarial Networks for Intrusion and Malware Detection 4. Harnessing Generative AI for Enhanced Cybersecurity in E-Health Systems 5. Leveraging Generative AI for Enhancing SDN Security: A Two-Level Classification Framework Against Relay-Based Link Fabrication Attacks Part III: Sector-Specific Use Cases 6. Adoption of Artificial Intelligence in Smart Cities: Literature Review and Bibliometric Analysis 7. Securing AI in Agriculture: Emerging Cyber Threats, Vulnerabilities, and Resilience Strategies 8. Blockchain-SDN-Based Secure Architectures for Industrial IoT 9. Towards Next-Generation Cyber Threat Intelligence in Edge-IoT: A Synergy of Generative AI, Federated Learning, and Blockchain Part IV: Ethics, Education, and Future Directions 10. Higher Education and Generative Artificial Intelligence: Applications, Challenges, and Ethics 11. On Exploring Generative AI for Privacy Preservation in IoT Ecosystems 12. Navigating Dualities - The Role of Generative AI in Cybersecurity for Defense and Attacks 13. Generative AI-Driven Penetration Testing: Frameworks, Methodologies, and Ethical Considerations