Hiển thị biểu ghi dạng vắn tắt

dc.contributor.authorChakraborty, Rajdeep
dc.contributor.authorGhosh, Anupam
dc.contributor.authorKumar Mandal, Jyotsna
dc.date.accessioned2024-06-12T03:29:29Z
dc.date.available2024-06-12T03:29:29Z
dc.date.issued2024
dc.identifier.urihttps://thuvienso.hoasen.edu.vn/handle/123456789/15335
dc.descriptionxix. 480 pagesvi
dc.description.abstractOur 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
dc.language.isoenvi
dc.publisherScrivener Publishingvi
dc.titleMachine Learning Techniques and Analytics for Cloud Securityvi
dc.typeBookvi


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Hiển thị biểu ghi dạng vắn tắt