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Stream/Area:
-
Subject:
-
Paper ID:
IJSREM40510
Volume:
9 |
1 |
Jan 2025
Pages:
Publication Frequency:
Monthly (12 issues per year)
License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.
Artificial intelligence (AI) holds significant potential in predicting zero-day vulnerabilities—undisclosed software flaws that attackers exploit before they are identified or addressed by developers. These vulnerabilities pose a critical challenge to cybersecurity, as their detection often relies on reactive measures following an attack. By analyzing historical vulnerability data, AI and machine learning techniques can identify patterns and characteristics indicative of software components prone to zero-day vulnerabilities. This predictive approach enables the early identification of high-risk areas within software systems, providing an opportunity to address potential threats proactively. AI-driven solutions can enhance traditional cybersecurity measures by shifting the focus from reactive detection to anticipatory defense. Such advancements in predictive capabilities not only reduce the risk of exploitation but also strengthen the overall resilience of digital ecosystems in an increasingly complex threat landscape.
KEYWORDS
Publication Date
5/31/2025
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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The authors declare no conflict of interest related to this research work.
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