Zero Trust for Intrusion Detection System: A Systematic Literature Review

Alalmaie, AZ; Waheed, N; Alalyan, M; Nanda, P; Jia, W; He, X

Zero Trust for Intrusion Detection System: A Systematic Literature Review

Alalmaie, AZ Waheed, N

Alalyan, M Nanda, P

Jia, W

He, X

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Publisher:: INSTICC
Publication Type:: Conference Proceeding
Citation:: International Conference on Agents and Artificial Intelligence, 2024, 3, pp. 170-177
Issue Date:: 2024-01-01

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Field	Value	Language
dc.contributor.author	Alalmaie, AZ
dc.contributor.author	Waheed, N https://orcid.org/0000-0001-5679-5365
dc.contributor.author	Alalyan, M
dc.contributor.author	Nanda, P https://orcid.org/0000-0002-5748-155X
dc.contributor.author	Jia, W https://orcid.org/0000-0002-0940-3338
dc.contributor.author	He, X
dc.date	2024-02-24
dc.date.accessioned	2025-04-24T04:33:22Z
dc.date.available	2025-04-24T04:33:22Z
dc.date.issued	2024-01-01
dc.identifier.citation	International Conference on Agents and Artificial Intelligence, 2024, 3, pp. 170-177
dc.identifier.issn	2184-3589
dc.identifier.issn	2184-433X
dc.identifier.uri	http://hdl.handle.net/10453/187026
dc.description.abstract	Organizations today are facing increasing cybersecurity challenges by moving more services to the cloud and outsourcing Intrusion Detection System (IDS) network monitoring tasks to third-party analysts. Zero Trust models may mitigate these challenges by employing the philosophy of “Never Trust, Always Verify.” However, specific anonymization approaches are required to ensure information integrity while preserving privacy. This paper reviews the existing approaches identified in the literature, compares them, and assesses the privacy-accuracy trade-offs. Plus, we have discussed future research directions and knowledge gaps.
dc.language	en
dc.publisher	INSTICC
dc.relation.ispartof	International Conference on Agents and Artificial Intelligence
dc.relation.ispartof	Proceedings of the 16th International Conference on Agents and Artificial Intelligence
dc.relation.isbasedon	10.5220/0012312300003636
dc.rights	info:eu-repo/semantics/openAccess
dc.title	Zero Trust for Intrusion Detection System: A Systematic Literature Review
dc.type	Conference Proceeding
utslib.citation.volume	3
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
pubs.organisational-group	University of Technology Sydney/UTS Groups
pubs.organisational-group	University of Technology Sydney/UTS Groups/Global Big Data Technologies Centre (GBDTC)
pubs.organisational-group	University of Technology Sydney/UTS Groups/The Trustworthy Digital Society
utslib.copyright.status	open_access	*
dc.rights.license	This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND 4.0). To view a copy of this license, visit https://creativecommons.org/licenses/by-nc-nd/4.0/
dc.date.updated	2025-04-24T04:33:21Z
pubs.finish-date	2024-02-26
pubs.publication-status	Published
pubs.start-date	2024-02-24
pubs.volume	3

Abstract:

Organizations today are facing increasing cybersecurity challenges by moving more services to the cloud and outsourcing Intrusion Detection System (IDS) network monitoring tasks to third-party analysts. Zero Trust models may mitigate these challenges by employing the philosophy of “Never Trust, Always Verify.” However, specific anonymization approaches are required to ensure information integrity while preserving privacy. This paper reviews the existing approaches identified in the literature, compares them, and assesses the privacy-accuracy trade-offs. Plus, we have discussed future research directions and knowledge gaps.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/187026