AI for Cybersecurity

Research and Practice

Houbing Herbert Song, Elisa Bertino, Alvaro Vasquez, Huihui Helen Wang, Yan Shoshitaishvili, Sumit Kumar Jha

• Publisher: Wiley-IEEE Press
• Year: 2026
• Print ISBN: 9781394293742
• eBook ISBN: 9781394293759
• Edition: 1
• Topic: Computer Science
• Subtopic: Artificial Intelligence (AI) & Semantics
• Index: Computer Science

Informative reference on the state of the art in cybersecurity and how to achieve a more secure cyberspace

AI for Cybersecurity presents the state of the art and practice in AI for cybersecurity with a focus on four interrelated defensive capabilities of deter, protect, detect, and respond. The book examines the fundamentals of AI for cybersecurity as a multidisciplinary subject, describes how to design, build, and operate AI technologies and strategies to achieve a more secure cyberspace, and provides why-what-how of each AI technique-cybersecurity task pair to enable researchers and practitioners to make contributions to the field of AI for cybersecurity.

This book is aligned with the National Science and Technology Council's (NSTC) 2023 Federal Cybersecurity Research and Development Strategic Plan (RDSP) and President Biden's Executive Order on the Safe, Secure, and Trustworthy Development and Use of Artificial Intelligence. Learning objectives and 200 illustrations are included throughout the text.

Written by a team of highly qualified experts in the field, AI for Cybersecurity discusses topics including:

• Robustness and risks of the methods covered, including adversarial ML threats in model training, deployment, and reuse
• Privacy risks including model inversion, membership inference, attribute inference, re-identification, and deanonymization
• Forensic and formal methods for analyzing, auditing, and verifying security- and privacy-related aspects of AI components
• Use of generative AI systems for improving security and the risks of generative AI systems to security
• Transparency and interpretability/explainability of models and algorithms and associated issues of fairness and bias

AI for Cybersecurity is an excellent reference for practitioners in AI for cybersecurity related industries such as commerce, education, energy, financial services, healthcare, manufacturing, and defense. Fourth year undergraduates and postgraduates in computer science and related programs of study will also find it valuable.

Table of contents

• Cover
• Table of Contents
• Title Page
• Copyright
• Dedication
• List of Contributors
• Foreword
• About the Editors
• Preface
• Acknowledgments
• Chapter 1: LLMs Are Not Few-shot Threat Hunters
• Chapter 2: LLMs on Support of Privacy and Security of Mobile Apps: State-of-the-art and Research Directions
• Chapter 3: Machine Learning-based Intrusion Detection Systems: Capabilities, Methodologies, and Open Research Challenges
• Chapter 4: Generative AI for Advanced Cyber Defense
• Chapter 5: Enhancing Threat Detection and Response with Generative AI and Blockchain
• Chapter 6: Privacy-preserving Collaborative Machine Learning
• Chapter 7: Security and Privacy in Federated Learning
• Chapter 8: Machine Learning Attacks on Signal Characteristics in Wireless Networks
• Chapter 9: Secure by Design
• Chapter 10: DDoS Detection in IoT Environments: Deep Packet Inspection and Real-world Applications
• Chapter 11: Data Science for Cybersecurity: A Case Study Focused on DDoS Attacks
• Chapter 12: AI Implications for Cybersecurity Education and Future Explorations
• Chapter 13: Ethical AI in Cybersecurity: Quantum-resistant Architectures and Decentralized Optimization Strategies
• Chapter 14: Security Threats and Defenses in AI-enabled Object Tracking Systems
• Chapter 15: AI for Android Malware Detection and Classification
• Chapter 16: Cyber-AI Supply Chain Vulnerabilities
• Chapter 17: AI-powered Physical Layer Security in Industrial Wireless Networks
• Chapter 18: The Security of Reinforcement Learning Systems in Electric Grid Domain
• Chapter 19: Geopolitical Dimensions of AI in Cybersecurity: The Emerging Battleground
• Chapter 20: Robust AI Techniques to Support High-consequence Applications in the Cyber Age
• Index
• End User License Agreement