Hewa Majeed Zangana, PhD, is an Assistant Professor at Duhok Polytechnic University, Iraq. He has held several academic and leadership positions including Acting Dean, Head of the Computer Science Department, and Director of the Curriculum Division at DPU. His research focuses on cybersecurity, intelligent systems, and AI-driven security solutions. He is also the editor of the forthcoming Defense in Depth: Modern Cybersecurity Strategies and Evolving Threats.

• About the Author xxxiiiPreface xxxvPart I Foundations of Cybersecurity 5.0 11 The Evolution of Cybersecurity: From Firewalls to AI 31.1 Introduction 31.2 Foundations of Early Cybersecurity 61.3 The Rise of Intrusion Detection and Prevention Systems (IDPSs) 81.4 The Emergence of Cloud and IoT Security Challenges 111.5 Machine Learning and Automation in Cyber Defense 141.6 AI- Driven Cybersecurity: The Modern Era (Cybersecurity 5.0) 171.7 Comparative Analysis: Traditional Versus AI- Based Cyber Defense 221.8 Challenges and Ethical Implications of AI in Cybersecurity 261.9 Future Trends and Directions 291.10 Conclusion 33References 342 The Cyber Threat Landscape in the AI Era 372.1 Introduction 372.2 Evolution of Cyber Threats: From Traditional to AI- Empowered Attacks 382.3 AI as a Double- Edged Sword in Cybersecurity 402.4 Emerging Categories of AI- Era Cyber Threats 422.5 Threat Actors and Motivations in the AI Era 442.6 AI- Driven Attack Vectors and Techniques 472.7 Defensive Strategies Against AI- Empowered Threats 502.8 Regulatory and Ethical Implications 532.9 Future Outlook of the Cyber Threat Landscape 552.10 Conclusion 57References 583 Fundamentals of Machine Learning and Data Analytics for Security 613.1 Introduction 613.2 Foundations of Machine Learning in Cybersecurity 633.3 Essential Algorithms and Models for Security Applications 683.4 Data Analytics in Cybersecurity 713.5 Feature Engineering and Data Preprocessing for Cybersecurity 763.6 Applications of ML and Data Analytics in Cybersecurity 803.7 Evaluation Metrics and Model Validation 843.8 Challenges and Limitations 873.9 Emerging Trends and Future Directions 923.10 Conclusion 94References 95Part II AI-Driven Cyber Defense 994 Intrusion Detection with Machine Learning 1014.1 Introduction 1014.2 Fundamentals of Intrusion Detection Systems (IDS) 1024.3 Role of Machine Learning in Intrusion Detection 1064.4 Datasets for Training and Evaluation 1094.5 Machine Learning Algorithms for Intrusion Detection 1124.6 Feature Engineering and Selection 1154.7 System Architecture of ML- Based IDS 1184.8 Evaluation Metrics and Performance Assessment 1204.9 Adversarial Attacks and Model Robustness 1234.10 Deployment and Real- World Applications 1264.11 Challenges and Future Trends 1284.12 Conclusion 131References 1315 Deep Learning for Malware and Ransomware Defense 1355.1 Introduction 1355.2 Understanding Malware and Ransomware 1375.3 Traditional Defense Mechanisms: Limitations and Challenges 1405.4 Role of Deep Learning in Cyber Defense 1435.5 Deep Learning Architectures for Malware Detection 1465.6 Model Training, Validation, and Evaluation 1515.7 Ransomware Detection and Behavior Analysis 1545.8 Adversarial Attacks on Deep Learning Models 1565.9 Deployment in Real- World Systems 1585.10 Case Studies and Experimental Results 1605.11 Future Directions and Research Challenges 1625.12 Conclusion 165References 1666 Adversarial AI and Defensive Countermeasures 1696.1 Introduction 1696.2 Understanding Adversarial AI 1716.3 Types of Adversarial Attacks 1726.4 Social Engineering- Enhanced Adversarial Attacks 1746.5 Adversarial Threats in Cybersecurity Systems 1756.6 Mechanisms of Adversarial Example Generation 1766.7 Evaluating AI System Robustness 1796.8 Defensive Countermeasures and Robust AI Strategies 1826.9 Model Explainability and Interpretability in Defense 1846.10 Adversarial AI in Reinforcement Learning and Autonomous Systems 1866.11 Human- in- the- Loop Defense Strategies 1896.12 Case Studies and Experimental Analysis 1916.13 Future Directions and Research Challenges 1936.14 Conclusion 196References 197Part III Emerging Technologies in Cybersecurity 5.0 2017 Blockchain for Secure and Transparent Systems 2037.1 Introduction 2037.2 Fundamentals of Blockchain Technology 2057.3 Blockchain Architectures and Types 2097.4 Consensus Mechanisms and Their Security Implications 2127.5 Blockchain in Cybersecurity: Applications and Use Cases 2167.6 Enhancing Transparency and Trust through Blockchain 2207.7 Integration of Blockchain with Artificial Intelligence 2237.8 Blockchain- Based Security Frameworks and Architectures 2267.9 Challenges and Limitations of Blockchain in Security 2297.10 Emerging Trends and Innovations 2327.11 Case Studies and Practical Implementations 2357.12 Future Research Directions 2377.13 Conclusion 239References 2408 IoT, Edge, and Cloud Security Challenges 2438.1 Introduction 2438.2 Understanding IoT, Edge, and Cloud Environments 2448.3 Security Threat Landscape 2478.4 IoT Security Challenges 2498.5 Edge Computing Security Issues 2518.6 Cloud Security Challenges 2548.7 Cross- Layer Security Integration 2568.8 Artificial Intelligence and Machine Learning in Security 2588.9 Blockchain and Zero Trust Architectures 2618.10 Regulatory and Compliance Considerations 2638.11 Emerging Trends and Future Research Directions 2658.12 Case Studies and Real- World Implementations 2688.13 Conclusion 271References 2729 Quantum- Safe Cryptography and Future- Proofing Security 2759.1 Introduction 2759.2 Background: Cryptography in the Pre- Quantum Era 2779.3 Quantum Computing and Its Threat to Cryptography 2819.4 Foundations of Quantum- Safe (Post- Quantum) Cryptography 2839.5 Quantum- Safe Cryptographic Algorithms and Techniques 2869.6 Integrating QSC in AI- Driven Security Systems 2899.7 Hybrid Cryptographic Models for Transitioning to Post- Quantum Security 2939.8 Quantum- Safe Security for Emerging Technologies 2969.9 Policy, Standards, and Regulatory Perspectives 2999.10 Challenges and Limitations 3019.11 Future Directions and Research Opportunities 3049.12 Case Studies and Applications 3079.13 Conclusion 310References 311Part IV Human and Organizational Dimensions 31510 Human Factors and Insider Threat Mitigation 31710.1 Introduction 31710.2 Understanding Human Factors in Cybersecurity 31810.3 Insider Threat Landscape 32110.4 Behavioral Indicators and Risk Assessment 32310.5 AI and Machine Learning for Insider Threat Detection 32510.6 Organizational Strategies for Insider Threat Mitigation 32710.7 Human– AI Collaboration in Cyber Defense 33010.8 Future Trends and Research Directions 33210.9 Challenges and Limitations 33410.10 Conclusion 336References 33711 Policy, Governance, and Ethical AI in Cyber Defense 33911.1 Introduction 33911.2 The Role of Policy and Governance in Cyber Defense 34111.3 AI Governance Models and Frameworks 34311.4 Ethical Considerations in AI- Driven Cyber Defense 34511.5 Legal and Regulatory Perspectives 34811.6 Responsible AI in Cybersecurity Operations 35011.7 Governance for Data Integrity and Model Security 35311.8 Policy Framework for AI- Enabled Cyber Defense Systems 35411.9 Ethical AI Decision- Making Framework 35711.10 Challenges and Future Directions 35911.11 Conclusion 361References 36212 Building Resilient and Self- Healing Cybersecurity Systems 36512.1 Introduction 36512.2 The Concept of Cyber Resilience 36712.3 Self- Healing Systems: Foundations and Mechanisms 37012.4 Architecture of a Self- Healing Cybersecurity System 37412.5 Role of Artificial Intelligence and Machine Learning 37912.6 Integration with Cybersecurity 5.0 Paradigm 38112.7 Implementation Challenges and Solutions 38412.8 Case Studies and Real- World Applications 38712.9 Future Trends and Research Directions 39012.10 Conclusion 392References 392Part V Future Directions 39513 Autonomous Cybersecurity: Toward Self- Defending Systems 39713.1 Introduction 39713.2 Understanding Autonomous Cybersecurity 39913.3 Core Components of a Self- Defending System 40313.4 The Role of Artificial Intelligence and Machine Learning 40613.5 Mechanisms of Self- Defense and Autonomy 41013.6 Integration Within Cybersecurity 5.0 Framework 41413.7 Architectural Framework for Autonomous Cyber Defense 41713.8 Key Technologies Enabling Autonomy 42113.9 Challenges and Limitations 42413.10 Case Studies and Practical Implementations 42813.11 Future Research Directions 43213.12 Conclusion 435References 43514 Case Studies Across Sectors (Finance, Healthcare, and Government) 43914.1 Introduction 43914.2 Methodology and Case Study Selection 44114.3 Cybersecurity 5.0 Overview Across Sectors 44414.4 Case Study 1: Financial Sector 44714.5 Case Study 2: Healthcare Sector 45114.6 Case Study 3: Government Sector 45414.7 Comparative Analysis Across Sectors 45714.8 Common Challenges and Mitigation Strategies 46014.9 Policy and Governance Implications 46314.10 Future Directions 46614.11 Conclusion 468References 46915 Roadmap to Cybersecurity 5.0 47315.1 Introduction 47315.2 Evolution of Cybersecurity Paradigms 47515.3 Defining Cybersecurity 5.0 47915.4 Core Pillars of Cybersecurity 5.0 48115.5 Strategic Roadmap and Development Phases 48515.6 Technological Enablers 48915.7 Organizational Transformation 49315.8 Policy, Regulation, and Ethics 49615.9 Challenges and Risk Factors 50015.10 Measuring Progress Toward Cybersecurity 5.0 50215.11 Vision for the Future 50415.12 Conclusion 506References 507Index 511