Enhancing Vulnerability Management with Artificial Intelligence Algorithms

The rising number of vulnerabilities, highlights the growing cybersecurity challenges and the need for robust vulnerability management. This paper examines the role of Artificial Intelligence in enhancing vulnerability detection and management, focusing on scalable and accurate solutions to address large-scale codebase analysis. AI-driven techniques bridge traditional static analysis and advanced detection, uncovering hidden vulnerabilities and improving efficiency. Future research should optimize these tools for diverse languages, Secure Software Development Life Cycle workflows, and predictive threat analysis. These advancements highlight AI's potential to strengthen software security in an increasingly complex threat landscape.

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Publication Details

Published
2024-11-21
DOI
https://doi.org/10.19107/cybercon.2024.13
Citations
1
Primary Topic
Anomaly Detection Techniques and Applications
Type
article
Field-Weighted Citation Impact
0.35
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article

Enhancing Vulnerability Management with Artificial Intelligence Algorithms

Sabina-Daniela Axinte, Ana-Maria Dincă, Ioan C. Bacivarov, Gabriela TOD-RĂILEANU
1 citations
Anomaly Detection Techniques and Applications
0.35
article

Enhancing Vulnerability Management with Artificial Intelligence Algorithms

Sabina-Daniela Axinte, Ana-Maria Dincă, Ioan C. Bacivarov, Gabriela TOD-RĂILEANU
article en
1 citations

Abstract

The rising number of vulnerabilities, highlights the growing cybersecurity challenges and the need for robust vulnerability management. This paper examines the role of Artificial Intelligence in enhancing vulnerability detection and management, focusing on scalable and accurate solutions to address large-scale codebase analysis. AI-driven techniques bridge traditional static analysis and advanced detection, uncovering hidden vulnerabilities and improving efficiency. Future research should optimize these tools for diverse languages, Secure Software Development Life Cycle workflows, and predictive threat analysis. These advancements highlight AI's potential to strengthen software security in an increasingly complex threat landscape.

Universitatea Națională de Știință și Tehnologie Politehnica București (RO)
Responsible consumption and production
Openalex Percentile: Top 31%
Anomaly Detection Techniques and Applications
0.35
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