دانلود کتاب هوش مصنوعی مولد برای امنیت سایبری و حریم خصوصی

The rapid pace of digital transformation has ushered in an era of unprecedented connectivity, innovation, and opportunity. Yet, it has also amplified the complexity and scale of cyber threats, challenging organizations, governments, and individuals to safeguard their digital assets and privacy in an increasingly volatile landscape. At the heart of this challenge lies the transformative potential of generative artificial intelligence (AI)—a technology capable of redefining how we detect, defend against, and mitigate cyber risks while preserving the integrity of sensitive data. Generative AI for Cybersecurity and Privacy is a comprehensive exploration of this dynamic intersection, offering a rigorous and forward-thinking guide to leveraging generative AI to build resilient, secure, and privacy-conscious digital ecosystems. This book emerges from a pressing need to address the evolving cybersecurity and privacy challenges posed by sophisticated adversaries, from advanced persistent threats to data breaches in interconnected systems. Generative AI, with its ability to model complex patterns, generate synthetic data, and adapt to emerging threats, represents a paradigm shift in how we approach security. It empowers defenders to anticipate and neutralize attacks proactively, enhances privacy-preserving mechanisms, and enables innovative applications across diverse domains. However, this power comes with dualities: generative AI can also be wielded by adversaries to craft novel attack vectors, underscoring the need for ethical frameworks and responsible deployment. This volume seeks to navigate these complexities, providing both theoretical depth and practical insights to empower a wide range of stakeholders. Structured in four meticulously curated parts, the book offers a comprehensive journey through the multifaceted role of generative AI in cybersecurity and privacy. Part I: Foundations and Core Concepts establishes the theoretical and conceptual groundwork. Chapter 1 provides a systematic review of machine learning and deep learning applications in information security, tracing the evolution of AI-driven defenses. Chapter 2 delves into the transformative impact of generative AI on cybersecurity and data privacy, setting the stage for the book’s exploration of cutting-edge methodologies. Part II: Applications in Threat Detection and Defense focuses on practical implementations, showcasing generative AI’s potential to enhance security operations. Chapter 3 explores the use of generative adversarial networks (GANs) for intrusion and malware detection, highlighting their ability to simulate and counter sophisticated threats. Chapter 4 examines how generative AI strengthens cybersecurity in e-health systems, ensuring the protection of sensitive medical data. Chapter 5 investigates the integration of generative AI with software-defined networking (SDN) to bolster network security, offering scalable solutions for dynamic environments. Part III: Sector-Specific Use Cases addresses the unique challenges of applying generative AI in critical domains. Chapter 6 aims to systematically classify existing research on AI within the smart city framework. Chapter 7 tackles cybersecurity in agriculture, identifying emerging threats, and proposing resilience strategies for AI-driven farming systems. Chapter 8 introduces blockchain-SDN-based architectures for securing industrial IoT, emphasizing robust and decentralized frameworks. Chapter 9 explores the synergy of generative AI, federated learning, and blockchain in edge-IoT environments, paving the way for next-generation cyber threat intelligence. Part IV: Ethics, Education, and Future Directions confronts the broader implications and future trajectories of generative AI. Chapter 10 examines its applications, challenges, and ethical considerations in higher education, fostering responsible adoption. Chapter 11 focuses on privacy preservation in IoT ecosystems, proposing generative AI-driven solutions to balance utility and confidentiality. Chapter 12 navigates the dual role of generative AI in cybersecurity, exploring its potential for both defense and attack. Finally, Chapter 13, Generative AI-Driven Penetration Testing: Frameworks, Methodologies, and Ethical Considerations, provides a detailed examination of how AI enhances proactive security testing, with a strong emphasis on ethical and legal accountability.