In an era where traditional firewalls and antivirus software no longer stand a chance against sophisticated cyberattacks, this book provides the essential roadmap you need to secure next-generation cloud, IoT, and decentralized systems against catastrophic data breaches and ransomware.

Over the past few years, traditional cybersecurity measures have proven insufficient against the ever-growing sophistication of cyberattacks. As technology advances, particularly with the proliferation of cloud computing, the Internet of Things, and decentralized applications, cybersecurity professionals are faced with new and more challenging security risks, ranging from data breaches to cyber-espionage and ransomware attacks. This book delves into the transformative impact of artificial intelligence and deep learning on contemporary cybersecurity practices. It provides a detailed exploration of how cutting-edge AI and deep learning methodologies are being leveraged to secure digital ecosystems, mitigate emerging threats, and strengthen cryptographic protocols.

By integrating traditional security approaches with advanced machine learning and neural networks, the book offers readers a robust understanding of the next-generation tools and frameworks that are shaping the cybersecurity landscape. In addition to theoretical foundations, the book features practical case studies and real-world applications. Readers will gain insights into the use of AI for securing cloud environments, IoT networks, and mobile platforms.

Emerging challenges such as securing AI models against adversarial attacks and ethical considerations in AI-driven cybersecurity are also discussed. With contributions from experts in academia and industry, the book offers a balanced perspective, making it a valuable resource for researchers, practitioners, and students at advanced undergraduate, graduate, and doctoral levels.

Shubham Mahajan, PhD is a Post Doctoral Reseacher in the Department of Applied Data Science at Noroff Univeristy College in Kristiansand, Norway. He holds eleven Indian patents, one Australian and one German patent and has published more than 70 articles in peer-reviewed journals and conferences. His research interests span a wide array of topics, encompassing image processing, video compression, image segmentation, fuzzy entropy, nature-inspired computing methods, optimization, data mining, machine learning, robotics, and optical communication.

Kamal Upreti, PhD is an Associate Professor in the Department of Computer Science at Christ University, Ghaziabad, India. He has published more than 50 patents, 32 magazine issues, 110 research papers in journals and international conferences, and 45 books. His areas of interest include modern physics, data analytics, cyber security, machine learning, healthcare, and embedded systems.

Rashmi Agrawal, PhD is a Professor and Associate Dean at the School of Computer Applications at the Manav Rahna International Institute of Research and Studies, Faridabad, India with more than 23 years of experience. She has published more than 100 research papers in esteemed national and international journals and conferences and authored and co-authored books with distinguished publishers. Her research focuses on machine learning, classification, and pattern recognintion.