| dc.description.abstract | Our objective in writing this book was to provide the reader with an in-depth knowledge
of how to integrate machine learning (ML) approaches to meet various analytical issues
in cloud security deemed necessary due to the advancement of IoT networks. Although
one of the ways to achieve cloud security is by using ML, the technique has long-standing
challenges that require methodological and theoretical approaches. Therefore, because the
conventional cryptographic approach is less frequently applied in resource-constrained
devices, the ML approach may be effectively used in providing security in the constantly
growing cloud environment. Machine learning algorithms can also be used to meet various
cloud security issues for effective intrusion detection and zero-knowledge authentication
systems. Moreover, these algorithms can also be used in applications and for much more,
including measuring passive attacks and designing protocols and privacy systems. This
book contains case studies/projects for implementing some security features based on ML
algorithms and analytics. It will provide learning paradigms for the field of artificial intelli-
gence and the deep learning community, with related datasets to help delve deeper into ML
for cloud security. | vi |
