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Cybersecurity Framework for Assessing the Efficiency of AI-Based Intrusion Detection Cybersecurity Techniques.

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	Cybersecurity Framework for Assessing the Efficiency of AI-Based Intrusion Detection Cybersecurity Techniques./
Author:	Ewan, Patricia Eugenie.
Description:	1 online resource (108 pages)
Notes:	Source: Dissertations Abstracts International, Volume: 85-12, Section: A.
Contained By:	Dissertations Abstracts International85-12A.
Subject:	Information technology. -
Online resource:	click for full text (PQDT)
ISBN:	9798383045435

Cybersecurity Framework for Assessing the Efficiency of AI-Based Intrusion Detection Cybersecurity Techniques.
Ewan, Patricia Eugenie.

Cybersecurity Framework for Assessing the Efficiency of AI-Based Intrusion Detection Cybersecurity Techniques. - 1 online resource (108 pages)

Source: Dissertations Abstracts International, Volume: 85-12, Section: A.

Thesis (Ph.D.)--National University, 2024.

Includes bibliographical references

The problem addressed by this study was the need for a practical cybersecurity framework that incorporates AI-driven advanced analytics that can be used to assess the feasibility and effectiveness of the AI-based cybersecurity techniques used for detecting network intrusions. The purpose of this qualitative exploratory and constructive research was to explore the effective AI-driven advanced analytics techniques that can be used to enhance the AI-based cybersecurity techniques already being used to detect network intrusions and propose a practical cybersecurity framework for assessing their feasibility. The research question answered was: In what ways and to what extent do AI-based advanced analytics impact the effectiveness of network intrusion detection systems? The data science framework developed by researchers from the University of Virginia's (UVA) Biocomplexity Institute and Initiative provided this study with a theoretical underpinning. This qualitative research explored how AI-based advanced analytics impact the effectiveness of AI-based network intrusion detection systems and proposed a practical cybersecurity framework for assessing the feasibility of the existing AI-based intrusion detection systems. Qualitative data were collected using interviews with 15 purposefully sampled participants. The collected data were thematically analyzed, identifying four themes: varied ways, varying usability, different measures, and people, process, and resources-related issues. The importance of the study was that it improved an understanding of the transformative potential of AI-based advanced analytics in network security and offered practical recommendations for organizations to utilize the power of the technology. Adopting AI-based advanced analytics requires training and skill development, regular signature updates, and balancing privacy concerns with security imperatives.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2024

Mode of access: World Wide Web

ISBN: 9798383045435Subjects--Topical Terms:

559429
Information technology.
Subjects--Index Terms:

Cybersecurity techniquesIndex Terms--Genre/Form:

554714
Electronic books.

Cybersecurity Framework for Assessing the Efficiency of AI-Based Intrusion Detection Cybersecurity Techniques.
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520 $a The problem addressed by this study was the need for a practical cybersecurity framework that incorporates AI-driven advanced analytics that can be used to assess the feasibility and effectiveness of the AI-based cybersecurity techniques used for detecting network intrusions. The purpose of this qualitative exploratory and constructive research was to explore the effective AI-driven advanced analytics techniques that can be used to enhance the AI-based cybersecurity techniques already being used to detect network intrusions and propose a practical cybersecurity framework for assessing their feasibility. The research question answered was: In what ways and to what extent do AI-based advanced analytics impact the effectiveness of network intrusion detection systems? The data science framework developed by researchers from the University of Virginia's (UVA) Biocomplexity Institute and Initiative provided this study with a theoretical underpinning. This qualitative research explored how AI-based advanced analytics impact the effectiveness of AI-based network intrusion detection systems and proposed a practical cybersecurity framework for assessing the feasibility of the existing AI-based intrusion detection systems. Qualitative data were collected using interviews with 15 purposefully sampled participants. The collected data were thematically analyzed, identifying four themes: varied ways, varying usability, different measures, and people, process, and resources-related issues. The importance of the study was that it improved an understanding of the transformative potential of AI-based advanced analytics in network security and offered practical recommendations for organizations to utilize the power of the technology. Adopting AI-based advanced analytics requires training and skill development, regular signature updates, and balancing privacy concerns with security imperatives.
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