Intelligent Cyber Defense: AI, Autonomous Systems, and the Future of Digital Security presents a comprehensive and multidisciplinary exploration of how artificial intelligence is redefining modern cybersecurity. As cyber threats grow in scale, complexity, and autonomy, conventional rule-based and signature-driven security mechanisms are increasingly inadequate. This book advances a new paradigm—intelligence-driven, adaptive, and trustworthy cyber defense systems—capable of anticipating, learning from, and responding to evolving threats in real time.
The volume brings together cutting-edge research and applied perspectives across key domains, including AI-enabled threat detection, reinforcement learning-based defense systems, federated and privacy-preserving learning, explainable artificial intelligence, behavioral biometrics, intelligent honeypots, malware evolution analysis, and AI-powered Security Operations Centers. Emerging application areas such as cloud security, Internet of Things (IoT) protection, zero-trust architectures, synthetic data generation, and metaverse security are examined in depth, reflecting the expanding digital attack surface of contemporary cyber ecosystems.
Special emphasis is placed on trust, transparency, ethics, and governance, addressing critical challenges such as model explainability, data privacy, bias mitigation, regulatory compliance, and responsible AI deployment. Through theoretical foundations, real-world case studies, benchmark datasets, and future outlooks, the book bridges the gap between academic research and operational cybersecurity practice.
Designed for researchers, postgraduate students, cybersecurity professionals, system architects, and policymakers, this edited volume serves as both a reference and a roadmap for building resilient, intelligent, and ethically grounded cyber defense infrastructures. It is a timely contribution for those seeking to understand and shape the future of AI-enabled cybersecurity in an increasingly interconnected and automated digital world.

1.Foundations of AI-Powered Cybersecurity:Integrating Machine Learning with Traditional Defense Architectures
2.The Role of Deep Learning in Identifying and Mitigating Advanced Persistent Threats (APTs)
3.Reinforcement Learning Techniques for Enhancing Cyber
4.Federated Learning in Cybersecurity: Enabling Privacy-Preserving Intrusion Detection System Across Distributed Environments 
5.A Comprehensive Framework for DDoS Intrusion Detection Using CICIDS 2017 Through Preprocessing, Modeling and Explainability with XGBoost and CICIDS 2017
6.AI-Driven Behavioural Biometrics for Continuous User Authentication and Fraud Prevention
7.AI-Augmented Threat Hunting- Leveraging Predictive Models for Cyber Defence 
8.Intelligent Honeypots: AI Techniques for Deception,Attack Attribution, and Intrusion Analysis
9.Machine Learning for Malware Classification and Evolutionary Threat Analysis 
10.The Application of Reinforcement Learning in Dynamic Cyber Defense and Intrusion Prevention
11.AI-Powered Security Operations Centers (SOCs):Redefining Cyber Defense through Automation and Intelligence 
12.Ethical Implications and Policy Considerations of Using Artificial Intelligence in Cybersecurity Domains.        
13.Zero Trust Architecture and AI: Building Self-Adaptive Security Ecosystems for Enterprise Environments
14.Autonomous Cyber Defense Agents: Opportunities and Challenges in Building AI-Driven Self- Defending Systems
15.Securing the Internet of Things (IoT) using Lightweight AI Models and Distributed Intelligence Mechanisms
16.AI in Cloud Security: Threat Detection, Compliance Monitoring, and Intelligent Response Automation
17.Synthetic Data Generation for Training AI Cybersecurity Models: Methods, Applications, and Limitations
18.Cybersecurity in the Metaverse: Leveraging AI to Safeguard Extended Reality (XR) Environments and Digital Avatars 
19.Transparent and Distributed Cyber Defense: Integrating Explainable AI with Federated Learning