Security in cloud computing has become a top priority in today’s digital age. With the increasing complexities and challenges of securing data stored in the cloud, new methodologies and technologies are constantly being developed to combat potential threats effectively. One such approach is the integration of machine learning techniques and analytics within cloud security solutions.
The book titled Machine Learning Techniques and Analytics for Cloud Security by Wiley delves into the realm of cloud security, offering insights into cutting-edge methods, surveys, case studies, and policies that harness machine learning for enhanced security measures. While traditional cryptography methods may fall short in resource-constrained environments, machine learning algorithms provide a promising alternative to bolster cloud security effectively.
By leveraging machine learning algorithms, various security issues within cloud environments can be addressed, including the development of robust intrusion detection systems, innovative authentication mechanisms, passive attack countermeasures, protocol enhancements, privacy system designs, and application security measures. Furthermore, the book features practical case studies and projects that demonstrate the implementation of machine learning algorithms and analytics in real-world cloud-based products across public, private, and hybrid cloud platforms.
Typically retailing for $194, Machine Learning Techniques and Analytics for Cloud Security is now available for free to BetaNews readers for a limited time, offering invaluable insights into the intersection of machine learning and cloud security. This resource serves as a comprehensive guide for understanding and implementing advanced security features to safeguard sensitive data in cloud environments effectively.
