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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.
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
