A Unified, Threat-Validated Taxonomy for Hardware Security Assurance

Hardware systems are foundational to critical infrastructure, embedded devices, and consumer products, making robust security assurance essential. However, existing hardware security standards remain fragmented, inconsistent in scope, and difficult to integrate, creating gaps in protection and inefficiencies in assurance planning. This paper proposes a unified, standard-aligned, and threat-validated taxonomy of Security Objective Domains (SODs) for hardware security assurance. The taxonomy was inductively derived from 1287 requirements across ten internationally recognized standards using AI-assisted clustering and expert validation, resulting in 22 domains structured by the Boundary-Driven System of Interest model. Each domain was then validated against 167 documented hardware-related threats from CWE/CVE databases, regulatory advisories, and incident reports. This threat-informed mapping enables quantitative analysis of assurance coverage, prioritization of high-risk areas, and identification of cross-domain dependencies. The framework harmonizes terminology, reduces redundancy, and addresses assurance gaps, offering a scalable basis for sector-specific profiles, automated compliance tooling, and evidence-driven risk management. Looking forward, the taxonomy can be extended with sector-specific standards, expanded threat datasets, and integration of weighted severity metrics such as CVSS to further enhance risk-based assurance.

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Publication Details

Journal
Journal of Cybersecurity and Privacy
Published
2025-10-13
DOI
https://doi.org/10.3390/jcp5040086
Citations
1
Primary Topic
Advanced Malware Detection Techniques
Type
article
Field-Weighted Citation Impact
1.49

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article

A Unified, Threat-Validated Taxonomy for Hardware Security Assurance

Shao-Fang Wen, Arvind Kumar Sharma
1 citations
Journal of Cybersecurity and Privacy
Advanced Malware Detection Techniques
1.49
article

A Unified, Threat-Validated Taxonomy for Hardware Security Assurance

Shao-Fang Wen, Arvind Kumar Sharma
article en
1 citations

Abstract

Hardware systems are foundational to critical infrastructure, embedded devices, and consumer products, making robust security assurance essential. However, existing hardware security standards remain fragmented, inconsistent in scope, and difficult to integrate, creating gaps in protection and inefficiencies in assurance planning. This paper proposes a unified, standard-aligned, and threat-validated taxonomy of Security Objective Domains (SODs) for hardware security assurance. The taxonomy was inductively derived from 1287 requirements across ten internationally recognized standards using AI-assisted clustering and expert validation, resulting in 22 domains structured by the Boundary-Driven System of Interest model. Each domain was then validated against 167 documented hardware-related threats from CWE/CVE databases, regulatory advisories, and incident reports. This threat-informed mapping enables quantitative analysis of assurance coverage, prioritization of high-risk areas, and identification of cross-domain dependencies. The framework harmonizes terminology, reduces redundancy, and addresses assurance gaps, offering a scalable basis for sector-specific profiles, automated compliance tooling, and evidence-driven risk management. Looking forward, the taxonomy can be extended with sector-specific standards, expanded threat datasets, and integration of weighted severity metrics such as CVSS to further enhance risk-based assurance.

Journal of Cybersecurity and PrivacyVol. 5(4)
Norwegian University of Science and Technology (NO), University of South-Eastern Norway (NO)
Norges Teknisk-Naturvitenskapelige Universitet, Science Foundation Ireland, Norges Forskningsråd, St. Olavs Hospital Universitetssykehuset i Trondheim
Openalex Percentile: Top 14%
Advanced Malware Detection Techniques
1.49
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A Unified, Threat-Validated Taxonomy for Hardware Security Assurance — Shao-Fang Wen, Arvind Kumar Sharma · Journal of Cybersecurity and Privacy (2025) | TGRS Research Map | TGRS