Publisher's Synopsis
In an era of ever-evolving cyber threats, traditional defenses are no longer enough. Machine Learning for Cyber Threat Detection offers a transformative guide into how AI-driven technologies are reshaping the cybersecurity landscape.
Curated and edited by Dr. Sanjay Agal, a recognized academic leader with over 16 years of experience in AI, cybersecurity, and data science, this book bridges the gap between foundational theory and real-world application. It provides students, professionals, and researchers with actionable insights into supervised and unsupervised learning, anomaly detection, malware analysis, phishing prevention, and securing IoT ecosystems.
Through practical case studies, cutting-edge methodologies, and expert commentary, readers will learn how machine learning models can predict, detect, and neutralize cyber threats in real time. Each chapter unfolds with clarity, offering step-by-step guidance that builds critical skills for the future of digital defense.
Whether you're beginning your journey in cybersecurity or looking to sharpen your expertise, this guide will empower you to think beyond conventional security models - and be prepared for the next generation of cyber challenges.
