Author(s)

Bharat Kumar Sukhwal, Vikas Dangi^*

Affiliation(s)

Janardan Rai Nagar Rajasthan Vidyapeeth, Udaipur, Rajasthan, India.

Corresponding Author

Vikas Dangi

ABSTRACT

Zero-day attacks pose a significant threat to cybersecurity, exploiting unknown vulnerabilities in software before they are discovered and patched. Traditional defense mechanisms struggle to detect these attacks due to their novel nature. This paper explores the potential of Artificial Intelligence (AI) in detecting and mitigating zero-day attacks. It reviews recent advancements in AI techniques, such as machine learning (ML), deep learning, and anomaly detection, that aim to predict and prevent zero-day vulnerabilities. By analyzing the strengths and limitations of these approaches, this paper outlines future directions for AI-driven solutions in the fight against zero-day threats.

KEYWORDS

Artificial intelligence; Zero-day attacks; Cybersecurity; Machine learning; Deep learning

CITE THIS PAPER

Bharat Kumar Sukhwal, Vikas Dangi. Using artificial intelligence for detecting and mitigating Zero-day attacks: A review of emerging techniques. World Journal of Information Technology. 2024, 2(3): 43-48. DOI: https://doi.org/10.61784/wjit3005.

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