Modern Principles, Practices, and Algorithms for Cloud Security

In today's modern age of information, new technologies are quickly emerging and being deployed into the field of information technology. Cloud computing is a tool that has proven to be a versatile piece of software within IT. Unfortunately, the high usage of Cloud has raised many concerns related to privacy, security, and data protection that have prevented cloud computing solutions from becoming the prevalent alternative for mission critical systems. Up-to-date research and current techniques are needed to help solve these vulnerabilities in cloud computing. Modern Principles, Practices, and Algorithms for Cloud Security is a pivotal reference source that provides vital research on the application of privacy and security in cloud computing. While highlighting topics such as chaos theory, soft computing, and cloud forensics, this publication explores present techniques and methodologies, as well as current trends in cloud protection. This book is ideally designed for IT specialists, scientists, software developers, security analysts, computer engineers, academicians, researchers, and students seeking current research on the defense of cloud services. Title Page Copyright Page Book Series Dedication Editorial Advisory Board Table of Contents Detailed Table of Contents Preface Acknowledgment Chapter 1: Security- and Privacy-Aware Computing in Cloud With User Mobility Chapter 2: Recent Advances Delivered in Mobile Cloud Computing's Security and Management Challenges Chapter 3: Security for Cross-Tenant Access Control in Cloud Computing Chapter 4: Secure Identity-Based Proxy Signature With Computational Diffie-Hellman for Cloud Data Management Chapter 5: Cloud Computing Adoption Chapter 6: A Computational Approach for Secure Cloud Computing Environments Chapter 7: Security Threats and Recent Countermeasures in Cloud Computing Chapter 8: A Framework Strategy to Overcome Trust Issues on Cloud Computing Adoption in Higher Education Chapter 9: A Proposal of Improvement for Transmission Channels in Cloud Environments Using the CBEDE Methodology Chapter 10: Meta-Heuristic and Non-Meta-Heuristic Energy-Efficient Load Balancing Algorithms in Cloud Computing Chapter 11: Distributed Algorithms for Finding Meta-Paths of a Large Heterogeneous Information Network on Cloud Chapter 12: A Tree Computing Block Chain Protocol Crypto-Signature Model Chapter 13: Automate Model Transformation From CIM to PIM up to PSM in Model-Driven Architecture Compilation of References Related References About the Contributors Index With the proliferation of devices connected to the internet and connected to each other, the volume of data collected, stored, and processed is increasing every day, which brings new challenges in terms of information security. As big data expands with the help of public clouds, traditional security solutions tailored to private computing infrastructures and confined to a well-defined security perimeter, such as firewalls and demilitarized zones (DMZs), are no longer effective. New security functions are required to work over the heterogenous composition of diverse hardware, operating systems, and network domains. Security, Privacy, and Forensics Issues in Big Data is an essential research book that examines recent advancements in big data and the impact that these advancements have on information security and privacy measures needed for these networks. Highlighting a range of topics including cryptography, data analytics, and threat detection, this is an excellent reference source for students, software developers and engineers, security analysts, IT consultants, academicians, researchers, and professionals. As the advancement of technology continues, cyber security continues to play a significant role in today's world. With society becoming more dependent on the internet, new opportunities for virtual attacks can lead to the exposure of critical information. Machine and deep learning techniques to prevent this exposure of information are being applied to address mounting concerns in computer security. The Handbook of Research on Machine and Deep Learning Applications for Cyber Security is a pivotal reference source that provides vital research on the application of machine learning techniques for network security research. While highlighting topics such as web security, malware detection, and secure information sharing, this publication explores recent research findings in the area of electronic security as well as challenges and countermeasures in cyber security research. It is ideally designed for software engineers, IT specialists, cybersecurity analysts, industrial experts, academicians, researchers, and post-graduate students. "This book explores the quality parameters of steganography techniques. It describes the modern steganography techniques in spatial domain using the principles of substitution, pixel value differencing (PVD), and exploiting modification directions (EMD). It also examines the various applications of steganography techniques"-- Provided by publisher Examines recent advances in big data and the impact that these advances have on information security and privacy measures needed for these networks. The book covers a range of topics, including cryptography, data analytics, and threat detection. "This book focuses on the security, privacy, and forensics of big data. It also examines the principles, algorithms, challenges and applications related to the security, privacy, and forensics of big data"-- Provided by publisher "This book explores the use of machine learning and deep learning applications in the areas of cyber security and cyber-attack handling mechanisms"-- Provided by publisher "This book examines the principles, algorithms, applications, and practices of security in cloud computing"-- Provided by publisher