AI and Machine Learning for Cybersecurity Engineering: Detect Advanced Threats, Minimize False Alerts, and Build Scalable Intelligent Defenses.Are you struggling to keep pace with attackers who move faster than rules, signatures, and manual analysis can handle?
Modern cyber threats are adaptive, automated, and relentless-and traditional security approaches are buckling under alert fatigue, false positives, and operational complexity. Security teams need systems that learn, scale, and respond at machine speed.
AI and Machine Learning for Cybersecurity Engineering delivers exactly that.
This book shows how to engineer practical, production-ready AI security systems that work in real environments. It focuses on how machine learning is actually used today to detect advanced threats, reduce noise, automate response, and harden defenses across networks, endpoints, cloud platforms, and identity systems. Every concept is tied to real operational use, proven patterns, and engineering decisions you can apply immediately.
Rather than treating AI as an abstract idea, this book frames it as an engineering discipline. You'll learn how security data flows through pipelines, how models are trained and tested against real adversaries, how automation is safely deployed, and how AI systems are governed, monitored, and defended against abuse. The result is a clear, actionable blueprint for building intelligent defenses that hold up under pressure.
By reading this book, you will gain the ability to:
Design AI-driven threat detection systems for networks, endpoints, malware, phishing, fraud, and cloud environments
Engineer data pipelines, features, and models that perform reliably in noisy, adversarial conditions
Reduce false alerts while preserving detection accuracy and response speed
Build autonomous and human-in-the-loop security automation that scales safely
Test, harden, and defend machine learning models against evasion, poisoning, and abuse
Deploy, monitor, and tune AI security systems in production with confidence
Measure security effectiveness using metrics that matter to SOCs and leadership
Apply governance, compliance, and ethical controls directly through engineering workflows
Written for developers, security engineers, and technical leaders, this book favors clarity, precision, and real-world execution over theory. It is designed to become a long-term reference-something you return to when building, troubleshooting, or scaling AI-powered security systems.
If you're ready to move beyond reactive security and start engineering defenses that learn, adapt, and respond at scale, AI and Machine Learning for Cybersecurity Engineering is the book to keep on your desk.
Get your copy today and start building intelligent defenses that actually work.
