Develops an AIBOM framework extending CycloneDX with AI-specific provenance metadata and an autonomous pipeline that achieves 98.7% reproducibility fidelity and 96.2% vulnerability match precision in containerised workflows.
A comprehensive survey: Evaluating the efficiency of ar- tificial intelligence and machine learning techniques on cyber security solutions
2 Pith papers cite this work. Polarity classification is still indexing.
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UNVERDICTED 2representative citing papers
MVRAF framework quantifies CVE risks via weighted CVSS aggregation, statistical correlation analysis, and empirical cumulative distributions, classifying 46.2% of network-based vulnerabilities as high-risk with strong CIA-severity links.
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Operationalising Artificial Intelligence Bills of Materials (AIBOMs) for Verifiable AI Provenance and Lifecycle Assurance
Develops an AIBOM framework extending CycloneDX with AI-specific provenance metadata and an autonomous pipeline that achieves 98.7% reproducibility fidelity and 96.2% vulnerability match precision in containerised workflows.
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Policy-Driven Vulnerability Risk Quantification framework for Large-Scale Cloud Infrastructure Data Security
MVRAF framework quantifies CVE risks via weighted CVSS aggregation, statistical correlation analysis, and empirical cumulative distributions, classifying 46.2% of network-based vulnerabilities as high-risk with strong CIA-severity links.