Machine learning boosts the capabilities of security solutions in the modern cyber environment. However, there are also security concerns associated with machine learning models and approaches: the vulnerability of machine learning models to adversarial attacks is a fatal flaw in the artificial intelligence technologies, and the privacy of the data used in the training and testing periods is also causing increasing concern among users. This book reviews the latest research in the area, including effective applications of machine learning methods in cybersecurity solutions and the urgent security risks related to the machine learning models. The book is divided into three parts: Cyber Security Based on Machine Learning; Security in Machine Learning Methods and Systems; and Security and Privacy in Outsourced Machine Learning. Addressing hot topics in cybersecurity and written by leading researchers in the field, the book features self-contained chapters to allow readers to select topics that are relevant to their needs. It is a valuable resource for all those interested in cybersecurity and robust machine learning, including graduate students and academic and industrial researchers, wanting to gain insights into cutting-edge research topics, as well as related tools and inspiring innovations.

Cloud computing adoption has revolutionized how businesses and individuals harness the power of technology. The cloud's scalability, accessibility, and cost-efficiency have propelled it to the forefront of modern computing paradigms. However, as organizations increasingly rely on cloud services to store, process, and manage their data and applications, an intricate web of challenges has emerged, casting shadows over the very foundations of cloud computing. Improving Security, Privacy, and Trust in Cloud Computing unravels the complexities surrounding the cloud landscape, delving into the core concerns of security, privacy, and trust that have come to define its evolution. It aims to equip readers with the insights, knowledge, and practical strategies needed to navigate the intricate realm of cloud computing while safeguarding their most valuable assets. This book's exploration into security, privacy, and trust in cloud computing takes a holistic approach. Throughout the chapters of this book, readers will embark on a multidimensional expedition. This book will take them through real-world case studies of successful cloud security implementations and unfortunate breaches that underscore the urgency of robust defenses. From data encryption techniques to incident response protocols, this book offers practical insights and actionable strategies that can be implemented by IT professionals, security experts, and decision-makers alike.

Smart Sensor Networks (WSNs) using AI have left a mark on the lives of all by aiding in various sectors, such as manufacturing, education, healthcare, and monitoring of the environment and industries. This book covers recent AI applications and explores aspects of modern sensor technologies and the systems needed to operate them. The book reviews the fundamental concepts of gathering, processing, and analyzing different AI-based models and methods. It covers recent WSN techniques for the purpose of effective network management on par with the standards laid out by international organizations in related fields and focuses on both core concepts along with major applicational areas. The book will be used by technical developers, academicians, data sciences, industrial professionals, researchers, and students interested in the latest innovations on problem-oriented processing techniques in sensor networks using IoT and evolutionary computer applications for Industry 4.0.

As today’s world continues to advance, Artificial Intelligence (AI) is a field that has become a staple of technological development and led to the advancement of numerous professional industries. An application within AI that has gained attention is machine learning. Machine learning uses statistical techniques and algorithms to give computer systems the ability to understand and its popularity has circulated through many trades. Understanding this technology and its countless implementations is pivotal for scientists and researchers across the world. The Handbook of Research on Emerging Trends and Applications of Machine Learning provides a high-level understanding of various machine learning algorithms along with modern tools and techniques using Artificial Intelligence. In addition, this book explores the critical role that machine learning plays in a variety of professional fields including healthcare, business, and computer science. While highlighting topics including image processing, predictive analytics, and smart grid management, this book is ideally designed for developers, data scientists, business analysts, information architects, finance agents, healthcare professionals, researchers, retail traders, professors, and graduate students seeking current research on the benefits, implementations, and trends of machine learning.

The ever-evolving wireless technology industry is demanding new technologies and standards to ensure a higher quality of experience for global end-users. This developing challenge has enabled researchers to identify the present trend of machine learning as a possible solution, but will it meet business velocity demand? Next-Generation Wireless Networks Meet Advanced Machine Learning Applications is a pivotal reference source that provides emerging trends and insights into various technologies of next-generation wireless networks to enable the dynamic optimization of system configuration and applications within the fields of wireless networks, broadband networks, and wireless communication. Featuring coverage on a broad range of topics such as machine learning, hybrid network environments, wireless communications, and the internet of things; this publication is ideally designed for industry experts, researchers, students, academicians, and practitioners seeking current research on various technologies of next-generation wireless networks.

As smart cities become more prevalent, the need for explainable AI (XAI) applications has become increasingly important. Advances in Explainable AI Applications for Smart Cities is a co-edited book that showcases the latest research and development in XAI for smart city applications. This book covers a wide range of topics, including medical diagnosis, finance and banking, judicial systems, military training, manufacturing industries, autonomous vehicles, insurance claim management, and cybersecurity solutions. Through its diverse case studies and research, this book provides valuable insights into the importance of XAI in smart city applications. This book is an essential resource for undergraduate and postgraduate students, researchers, academicians, industry professionals, and scientists working in research laboratories. It provides a comprehensive overview of XAI concepts, advantages over AI, and its applications in smart city development. By showcasing the impact of XAI on various smart city applications, the book enables readers to understand the importance of XAI in creating more sustainable and efficient smart cities. Additionally, the book addresses the open challenges and research issues related to XAI in modern smart cities, providing a roadmap for future research in this field. Overall, this book is a valuable resource for anyone interested in understanding the importance of XAI in smart city applications.

This volume comprises the select proceedings of the International Conference on Information Security, Privacy, and Digital Forensics (ICISPD 2022). The content discusses novel contributions and latest developments in cyber-attacks and defenses, computer forensics and cybersecurity database forensics, cyber threat intelligence, data analytics for security, anonymity, penetration testing, incident response, Internet of Things security, malware and botnets, social media security, humanitarian forensics, software and media piracy, crime analysis, hardware security, among others. This volume will be a useful guide for researchers across industry and academia working in the field of security, privacy, and digital forensics from both technological and social perspectives.

The fusion of drones and Industry 5.0 has emerged as a transformative force, redefining the landscape of industrial progress. Drone Applications for Industry 5.0 reveals the strong connection between drones and Industry 5.0, exploring how they come together to blend human skills with automated precision. As we stand on the horizon of the fifth industrial revolution, Industry 5.0 uniquely celebrates the return of the human touch, harmonizing the strengths of machines with human intuition and empathy. Drones play a pivotal role in shaping this evolutionary transition. The narrative unfolds against the backdrop of historical industrial revolutions, each marked by radical transformations. Unlike its predecessors, Industry 5.0 places humans at the center, emphasizing collaboration with machines. Drones have matured into invaluable instruments with applications spanning manufacturing, agriculture, transportation, and emergency services. Drone Applications for Industry 5.0 embarks on a journey, guiding scholars, researchers, and students through the foundations of Industry 5.0 and the mechanics of drones. It explores practical uses in various fields, offering both theory and practical insights which empowers professionals to fully utilize drones.