Performance of Machine Learning and Big Data Analytics paradigms in Cybersecurity and Cloud Computing Platforms

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Professor Gabriel Kabanda
Professor Gabriel Kabanda

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GJCST Volume 21 Issue G2

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The purpose of the research is to evaluate Machine Learning and Big Data Analytics paradigms for use in Cybersecurity. Cybersecurity refers to a combination of technologies, processes and operations that are framed to protect information systems, computers, devices, programs, data and networks from internal or external threats, harm, damage, attacks or unauthorized access. The main characteristic of Machine Learning (ML) is the automatic data analysis of large data sets and production of models for the general relationships found among data. ML algorithms, as part of Artificial Intelligence, can be clustered into supervised, unsupervised, semi-supervised, and reinforcement learning algorithms.

Funding

No external funding was declared for this work.

Conflict of Interest

The authors declare no conflict of interest.

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No ethics committee approval was required for this article type.

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Professor Gabriel Kabanda. 2021. \u201cPerformance of Machine Learning and Big Data Analytics paradigms in Cybersecurity and Cloud Computing Platforms\u201d. Global Journal of Computer Science and Technology - G: Interdisciplinary GJCST-G Volume 21 (GJCST Volume 21 Issue G2): .

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Enhanced ALT text: Research paper on big data analytics paradigms in cybersecurity and cloud computing.
Journal Specifications

Crossref Journal DOI 10.17406/gjcst

Print ISSN 0975-4350

e-ISSN 0975-4172

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GJCST-G Classification: K.4.4
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v1.2

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August 20, 2021

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English

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The purpose of the research is to evaluate Machine Learning and Big Data Analytics paradigms for use in Cybersecurity. Cybersecurity refers to a combination of technologies, processes and operations that are framed to protect information systems, computers, devices, programs, data and networks from internal or external threats, harm, damage, attacks or unauthorized access. The main characteristic of Machine Learning (ML) is the automatic data analysis of large data sets and production of models for the general relationships found among data. ML algorithms, as part of Artificial Intelligence, can be clustered into supervised, unsupervised, semi-supervised, and reinforcement learning algorithms.

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Performance of Machine Learning and Big Data Analytics paradigms in Cybersecurity and Cloud Computing Platforms

Professor Gabriel Kabanda
Professor Gabriel Kabanda

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