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

Professor Gabriel Kabanda

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

Professor Gabriel Kabanda *

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

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Abstract

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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Funding

No external funding was declared for this work.

Conflict of Interest

The authors declare no conflict of interest.

Ethical Approval

No ethics committee approval was required for this article type.

Data Availability

Not applicable for this article.

How to Cite This Article

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

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GJCST Volume 21 Issue G2

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Journal Specifications

Crossref Journal DOI 10.17406/gjcst

Print ISSN 0975-4350

e-ISSN 0975-4172

Keywords

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Classification

GJCST-G Classification K.4.4

Submission ReceivedSeptember 9, 2021
Peer Review Double Blind
Handling Editor
Accepted October 4, 2021
Published October 15, 2021

Version of record

v1.2

Issue date

August 20, 2021

Language

English

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