Generative AI for Cybersecurity by Eddine, Boubiche Djallel; Akleylek, Sedat;

Short description:

Generative AI for Cybersecurity explores how rapidly evolving generative models are reshaping modern digital defense. As organizations become more interconnected and data-driven, traditional cybersecurity measures are increasingly challenged by adaptive, AI-powered threats. 

Long description:

Generative AI for Cybersecurity explores how rapidly evolving generative models are reshaping modern digital defense. As organizations become more interconnected and data-driven, traditional cybersecurity measures are increasingly challenged by adaptive, AI-powered threats. Generative AI introduces new capabilities that can significantly enhance threat detection, automate security operations, and improve situational awareness, but it also enables sophisticated offensive techniques, deepfakes, automated malware generation, and large-scale misinformation.

This book provides a balanced and comprehensive examination of this dual-use technology. It highlights how generative models can be leveraged to build resilient, intelligent, and proactive defense mechanisms capable of anticipating and countering emerging cyber risks. At the same time, it critically analyzes the vulnerabilities, ethical dilemmas, and regulatory challenges introduced by the misuse of generative AI. Through diverse perspectives and expert contributions, the book bridges theoretical foundations with real-world applications, demonstrating how GenAI can support adaptive intrusion detection, anomaly analysis, secure autonomous systems, and more transparent and explainable security solutions.

Beyond technical considerations, the book addresses broader societal, geopolitical, and governance implications, including issues of trust, sovereignty, and responsible AI deployment. It offers frameworks, methodologies, and practical insights suitable for researchers, practitioners, students, and policymakers seeking to understand, develop, or regulate GenAI-driven cybersecurity systems.

By examining both the opportunities and the risks, Generative AI for Cybersecurity serves as a timely reference for navigating an era where AI is not only a tool for defense but also a catalyst for new forms of cyber aggression, highlighting the urgent need for innovative, ethical, and resilient approaches to securing the digital world.

Table of Contents:

Part 1: Introduction and Background. Chapter 1 The Rise of Generative AI in Cybersecurity: Balancing Benefits and Risks. Part 2: The Power of Generative AI in Cyber Defense. Chapter 2 Adversarial Intelligence: Leveraging Generative Models for Cyber Defense and Resilience. Chapter 3 Generative AI in Cybersecurity Operations: Advancing Defence and Building Trust. Chapter 4 Generative AI for Indigenous Cyber Defense: Building Sovereign Digital Resilience in the Global South. Part 3: Weaponized Generative AI. Chapter 5 The Dark Side of Generative AI: Threats and Risks. Chapter 6 Generative AI in Offensive Security: Capabilities, Challenges, and Risks. Chapter 7 The Dark Side of Generative AI: Detecting Deep Fakes through Emotion Analysis. Part 4: Proactive Cybersecurity with Generative AI. Chapter 8 Proactive Cybersecurity with Generative AI. Chapter 9 GEPARD: A GenAI-Enabled Proactive Adaptive Resilient Defense Framework for Cybersecurity. Part 5: Case uses of Generative AI in Cybersecurity. Chapter 10 Host-Based Intrusion Detection Systems Developed Using ADFA-LD and AWSCTD Datasets. Chapter 11 An Explainable Intelligence Framework for IoT Anomaly Detection Using Hierarchical Feature Embedding and Latent Space Modelling. Chapter 12 Accurate and Lightweight IoV Intrusion Detection: Correlation-Filtered Ensemble Feature Selection.