Artificial Intelligence and Machine Learning are transforming cybersecurity at an unprecedented pace. Security professionals are no longer limited to traditional rule-based detection systems. Today, intelligent models can detect anomalies, identify unknown threats, automate incident response, and even simulate sophisticated cyberattacks.
AI/ML in Cybersecurity Engineering: Defensive and Offensive Applications provides a practical and engineering-focused guide to how modern machine learning techniques are applied across the cybersecurity landscape. The book explores both defensive security applications—such as intrusion detection, malware analysis, threat intelligence, and security automation—as well as offensive use cases including AI-assisted penetration testing, adversarial machine learning, and automated exploit generation.
Rather than focusing purely on theory, this book explains how security engineers can design, implement, and evaluate AI-driven security systems using real-world architectures and engineering practices.
Inside this book you will learn:
• How machine learning models are used in intrusion detection and anomaly detection
• Techniques for AI-based malware analysis and endpoint protection
• How deep learning improves phishing detection and threat intelligence
• Offensive applications such as automated vulnerability discovery and AI-driven reconnaissance
• The risks of adversarial machine learning and how attackers manipulate AI systems
• How to build resilient, explainable, and trustworthy AI security architectures
This book is designed for cybersecurity professionals, security engineers, researchers, and technical leaders who want to understand how artificial intelligence is shaping the future of cyber defense and cyber offense.
The goal of this book is simple: to bridge the gap between cybersecurity engineering and practical machine learning implementation.
