AI-Enabled Cyber Threats

The Rise of Artificial Intelligence in Cybercrime
1. Auflage 2026
ISBN: 978-1-394-41694-3
Verlag: John Wiley & Sons Inc

Buch, Englisch, 288 Seiten

Understand AI-driven cyberattacks and build effective defensive strategies against them

Artificial intelligence is increasingly used to automate, enhance, and evolve cyberattacks, yet most existing resources address AI only as a defensive tool. AI-Enabled Cyber Threats: The Rise of Artificial Intelligence in Cybercrime delivers a full-spectrum analysis of AI’s dual role in cybersecurity, covering offensive techniques, adversarial AI methods, and countermeasures from technical, ethical, and societal perspectives for professional and academic audiences.

AI-Enabled Cyber Threats balances theoretical foundations with actionable defensive strategies. It examines the sophisticated methods malicious actors employ using AI, from autonomous AI hackers to AI-powered nation-state warfare. The book addresses the dual-use nature of AI technologies, equipping readers to design stronger defenses, understand adversarial AI techniques, and lead security innovations responsibly in an AI-dominant threat landscape.

The book also provides: - Analysis of how AI automates and evolves cyberattack methodologies, including practical threat examples drawn from real-world offensive scenarios
- Defensive frameworks and countermeasures developed by security professionals and institutions to mitigate AI-driven threats across organizations
- Coverage of ethical considerations and societal impacts arising from the weaponization of artificial intelligence in cybercrime
- Future-facing insights on emerging risks including autonomous AI hackers and AI-powered nation-state cyber warfare campaigns
- Guidance for building proactive, adaptive cybersecurity strategies that anticipate threats rather than relying on traditional reactive approaches

Designed for cybersecurity professionals, AI researchers, and graduate students studying adversarial machine learning or cybercrime and digital forensics, this book provides the technical depth and strategic perspective needed to understand and counter AI-driven threats. Risk managers and compliance professionals will also find useful frameworks for organizational defense.

Autoren/Hrsg.

Dommari, Sandeep

Fachgebiete

• Mathematik | Informatik EDV | Informatik Programmierung | Softwareentwicklung Software Engineering
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz
• Mathematik | Informatik EDV | Informatik Computerkommunikation & -vernetzung Netzwerksicherheit

Inhaltsverzeichnis

Preface xi
Acknowledgements xvii
About the Companion Website xix

1 Introduction to Artificial Intelligence-enabled Cyber Threats 1
1.1 The Convergence of Artificial Intelligence and Cybersecurity 1
1.2 Historical Evolution of Cyber Threats 3
1.3 The AI Revolution in Cybercrime 5
1.4 Defining AI-enabled Threats 6
1.5 Scope and Scale of the Problem 8
1.6 Current Threat Landscape 9
1.7 Objectives of This Book 11
1.8 Target Audience and Structure 13
1.9 Terminology and Conventions 15
1.10 Looking Ahead 17

2 Fundamentals of Artificial Intelligence/Machine Learning in Cybersecurity Context 21
2.1 Machine Learning Basics 21
2.2 Supervised Learning Algorithms 23
2.3 Unsupervised Learning Algorithms 25
2.4 Deep Learning and Neural Networks 27
2.5 Natural Language Processing 30
2.6 Computer Vision and Generative Adversarial Networks 32
2.7 Reinforcement Learning 35
2.8 AI in Offensive Cybersecurity 37
2.9 AI in Defensive Cybersecurity 39
2.10 Code Examples and Implementations 41

3 Artificial Intelligence-enhanced Attack Vectors 47
3.1 AI-generated Phishing and Social Engineering 47
3.2 Large Language Models in Cybercrime 49
3.3 Spear-phishing Automation 53
3.4 Deepfake Technology 56
3.5 Voice Cloning Attacks 60
3.6 Detection Evasion Techniques 64
3.7 Case Studies and Statistics 67

4 Artificial Intelligence-powered Malware and Ransomware 75
4.1 Polymorphic Malware Using Machine Learning 75
4.2 Adversarial Machine Learning for Evasion 80
4.3 AI-generated Code Obfuscation 84
4.4 Ransomware-as-a-service with AI 88
4.5 Automated Vulnerability Exploitation 94
4.6 Major Ransomware Campaigns 98
4.7 Malicious AI Tools and Frameworks 104

5 Adversarial Machine Learning Attacks 113
5.1 Data Poisoning and Backdoor Attacks 113
5.2 Evasion Attacks and Adversarial Examples 116
5.3 Model Inversion and Privacy Attacks 119
5.4 Model Extraction and Stealing 122
5.5 Prompt Injection and LLM Attacks 125
5.6 AI System Vulnerabilities 128
5.7 Defense Mechanisms 130

6 Real-world Case Studies and Forensic Analysis 137
6.1 MGM Resorts Cyberattack 137
6.2 Colonial Pipeline Ransomware 141
6.3 Arup Engineering Deepfake Fraud 142
6.4 Supplementary Detailed Analysis 143
6.5 Threat Actor Profiles and TTPs 154

7 Defensive Artificial Intelligence Technologies and Countermeasures 159
7.1 Machine Learning-based Threat Detection 159
7.2 Anomaly Detection Algorithms 161
7.3 Behavioral Analytics and UEBA 164
7.4 Network Traffic Analysis (AI/ML Approaches) 165
7.5 AI-powered IDS/IPS Systems 167
7.6 Automated Threat Hunting 169
7.7 SIEM and SOAR with AI 171
7.8 Zero Trust Architecture 172
7.9 Deception Technologies 175
7.10 Real-world Implementations 176

8 Technical Implementation—Code Examples and Frameworks 185
8.1 TensorFlow for Cybersecurity 185
8.2 PyTorch for Threat Detection 187
8.3 Scikit-learn for Security Analytics 189
8.4 Building AI-powered IDS 191
8.5 Implementing Behavioral Analytics 194
8.6 Automated Response Systems 196
8.7 Adversarial Training 198
8.8 Model Hardening Techniques 200
8.9 Explainable AI for Security 202
8.10 MLOps for Security Deployment 204

9 Policy, Ethics, and Governance 211
9.1 Regulatory Frameworks 211
9.2 NIST AI Risk Management Framework 212
9.3 EU AI Act Implications 214
9.4 Ethical Considerations in AI-enabled Cyber Operations 217
9.5 Bias and Fairness in AI Security Systems 218
9.6 Privacy and Data Protection: GDPR, CCPA, and Beyond 220
9.7 Accountability and Transparency in AI Systems 221
9.8 AI Governance Framework for Cybersecurity 223
9.9 International Cooperation and Agreements 225
9.10 Industry Best Practices and Self-regulation 226

10 Future Trends and Emerging Threats 231
10.1 Introduction to the Future of AI in Cybercrime 231
10.2 The Quantum Computing Threat to Cryptography 232
10.3 PQC and the Transition 234
10.4 Next-generation AI Attacks: Swarm Intelligence and Self-evolving Malware 236
10.5 The Rise of Autonomous AI Agents in Cyber Warfare 238
10.6 Securing the Convergence: AI in IoT, Edge, 5G, and 6G Networks 240
10.7 The Role of Blockchain in Future Cybersecurity Architectures 242
10.8 Nation-state Cyber Warfare in the AI Era 243
10.9 Predictions for the Cyber Threat Landscape (2025–2030) 245
10.10 Conclusion: Recommendations for Organizational Resilience 247

References 248
Subject Index 253

Über Autor(innen)

SANDEEP DOMMARI is a recognized Cybersecurity, IAM, and AI leader with over 18 years of experience. Currently serving as a Principal Cybersecurity Architect, he designs scalable, secure digital ecosystems that protect millions globally.
Throughout his career across the public and private sectors, Sandeep has spearheaded large-scale IAM transformations and AI-driven threat detection initiatives. A passionate advocate for ethical AI and zero-trust security, he bridges cutting-edge technology with practical enterprise needs to develop proactive, resilient architectures.
His mission is to build secure, trusted digital environments that serve both business and society.