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- W4312793005 abstract "As a service-oriented architecture, cloud computing aims to be dynamic (SoA). The services in the SoA are provided in a variety of commercial domain like private, public and others. Virtualization is intended to provide the complete depiction of cloud computing systems at a reasonable pace. Nonetheless, the size and complexity of virtualized systems are proven to be troublesome in this process. These services must be secure and are therefore critical to the cloud architecture. To protect and sustain resilience in the cloud, it must be able to detect not just known attacks, but also emerging problems that target cloud infrastructure. This study describes a security analytics technique in virtualized infrastructure for identifying cloud computing threats. To categorize the attacked and non-attacked pictures, a deep learning method is proposed here. The detection phase is divided into two major steps: (i) feature extraction and (ii) classification. Proposed hole entropy characteristics are extracted during the feature extraction step. These collected characteristics are then submitted to the classification process, in which the improved deep belief network (DBN) is employed to identify a network assault. The weights are ideally modified using the Elephant Herding Optimization algorithm, which aids in DBN learning and improves detection accuracy." @default.
- W4312793005 created "2023-01-05" @default.
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- W4312793005 date "2022-01-01" @default.
- W4312793005 modified "2023-09-25" @default.
- W4312793005 title "Deep Elephant Herding Optimization-Based Attack Detection for Securing Virtualized Infrastructures of Cloud" @default.
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- W4312793005 doi "https://doi.org/10.1007/978-981-19-1653-3_31" @default.
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