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.
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Dr Sanjay Agal, Saswati Chatterjee, Dr. Sumit Soni, Dr. Mukesh Choudhary, Rinkal Sarvaiya
Dr. Sanjay Agal is a distinguished academic leader, researcher, and educator with over 16 years of extensive experience in the fields of Artificial Intelligence, Machine Learning, Cybersecurity, and Data Science. Currently serving as the Professor and Head of the Department of Artificial Intelligence and Data Science at Parul University, Vadodara, India, Dr. Agal has been at the forefront of integrating emerging technologies with industry-ready education.
A committed advocate for innovation and excellence, Dr. Agal has guided numerous research projects, published papers in reputed journals, and mentored hundreds of engineering and technology students towards academic and professional success. His areas of expertise include cybersecurity frameworks, AI-driven threat intelligence, blockchain integration, and advanced computing systems.
As the Editor of Machine Learning for Cyber Threat Detection: A Technical Guide for Students & Professionals, Dr. Agal brings a unique blend of scholarly rigor and real-world practicality. His vision is to bridge the critical gap between theoretical knowledge and its application in rapidly evolving digital ecosystems. His editorial guidance ensures that this book not only conveys complex technical concepts with clarity but also equips readers with actionable skills crucial for defending against modern cyber threats.
Dr. Agal's leadership extends beyond academics; he is actively involved in faculty development programs, national-level technical workshops, and collaborative research initiatives that promote the advancement of AI and cybersecurity domains in India and globally. His commitment to staying at the cutting edge of technological advancements has made him a trusted mentor, speaker, and advisor within academic and professional circles.
Passionate about empowering the next generation of cybersecurity experts, Dr. Agal believes in fostering critical thinking, adaptability, and ethical responsibility in an age of rapid technological change. His work reflects a deep understanding of how machine learning can be harnessed to build smarter, more resilient security infrastructures.
Through his editorial contribution to this guide, Dr. Sanjay Agal reaffirms his dedication to knowledge dissemination, inspiring students, researchers, and professionals to pioneer innovative solutions in the field of cybersecurity.
