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Cloud Computing Services, Data Analy...

Bhatta, Upakar,

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Cloud Computing Services, Data Analytics, and Machine Learning Technique for System Security and Cybersecurity Specialists /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Cloud Computing Services, Data Analytics, and Machine Learning Technique for System Security and Cybersecurity Specialists // Upakar Bhatta.
Author:	Bhatta, Upakar,
Description:	1 electronic resource (97 pages)
Notes:	Source: Dissertations Abstracts International, Volume: 86-05, Section: B.
Contained By:	Dissertations Abstracts International86-05B.
Subject:	Information technology. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31633945
ISBN:	9798342720106

Cloud Computing Services, Data Analytics, and Machine Learning Technique for System Security and Cybersecurity Specialists /
Bhatta, Upakar,

Cloud Computing Services, Data Analytics, and Machine Learning Technique for System Security and Cybersecurity Specialists /Upakar Bhatta. - 1 electronic resource (97 pages)

Source: Dissertations Abstracts International, Volume: 86-05, Section: B.

In today's dynamic and competitive digital era, companies are leveraging cloud technology, artificial intelligence, and data visualization techniques to reinvent their business processes. The combination of cloud technology, artificial intelligence, and data visualization techniques allows hybrid enterprise network to hold massive volumes of data, including both consumer and vendor information, and provide employees and customers easy access to these cloud data. These massive collections of complex data sets are facing security challenges. While cloud platforms are more vulnerable to security threats and traditional security technologies are unable to cope with the rapid data explosion in cloud platforms, machine learning powered security solutions and data visualization techniques are playing instrumental roles in detecting security threat, data breaches, and automatic finding software vulnerabilities. The purpose of this dissertation is to present some of the widely used cloud services, machine learning techniques and targeted data visualization and reporting approach to help network architect and organizational vulnerability remediation teams to detect some of the most threatening cyber threats to the organizations critical infrastructure. This dissertation outlines the industry standard machine learning tool used for anomaly detection. In this dissertation, I offer new insights into cyber risks and how public and private companies can leverage machine learning tools data visualization techniques to reduce cyber risks to their critical IT infrastructures and make better decision for vulnerability remediation.

English

ISBN: 9798342720106Subjects--Topical Terms:

532993
Information technology.
Subjects--Index Terms:

Cybersecurity

Cloud Computing Services, Data Analytics, and Machine Learning Technique for System Security and Cybersecurity Specialists /
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520 $a In today's dynamic and competitive digital era, companies are leveraging cloud technology, artificial intelligence, and data visualization techniques to reinvent their business processes. The combination of cloud technology, artificial intelligence, and data visualization techniques allows hybrid enterprise network to hold massive volumes of data, including both consumer and vendor information, and provide employees and customers easy access to these cloud data. These massive collections of complex data sets are facing security challenges. While cloud platforms are more vulnerable to security threats and traditional security technologies are unable to cope with the rapid data explosion in cloud platforms, machine learning powered security solutions and data visualization techniques are playing instrumental roles in detecting security threat, data breaches, and automatic finding software vulnerabilities. The purpose of this dissertation is to present some of the widely used cloud services, machine learning techniques and targeted data visualization and reporting approach to help network architect and organizational vulnerability remediation teams to detect some of the most threatening cyber threats to the organizations critical infrastructure. This dissertation outlines the industry standard machine learning tool used for anomaly detection. In this dissertation, I offer new insights into cyber risks and how public and private companies can leverage machine learning tools data visualization techniques to reduce cyber risks to their critical IT infrastructures and make better decision for vulnerability remediation.
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653 $a Data visualization
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