A Systematic Literature Review on AI-Based Methods and Challenges in Detecting Zero-Day Attacks

The detection of zero-day attacks remains one of the most critical challenges in cybersecurity. This systematic literature review focuses on the various AI-based methods employed for detecting zero-day attacks, identifying both the strengths and weaknesses of these approaches. By critically evaluati...

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Bibliographic Details
Published in:IEEE access 2024, Vol.12, p.144150-144163
Main Authors: Yee Por, Lip, Dai, Zhen, Juan Leem, Siew, Chen, Yi, Yang, Jing, Binbeshr, Farid, Yuen Phan, Koo, Soon Ku, Chin
Format: Article
Language:English
Subjects:
Anomaly detection
Artificial intelligence
CrowdStrike
Cybersecurity
Intrusion detection
Literature reviews
Machine learning
Object recognition
Real time
Search problems
Systematics
Zero-day attack
Citations: Items that this one cites
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Summary:The detection of zero-day attacks remains one of the most critical challenges in cybersecurity. This systematic literature review focuses on the various AI-based methods employed for detecting zero-day attacks, identifying both the strengths and weaknesses of these approaches. By critically evaluating existing literature, this review provides new insights and highlights the gaps that future research must address. The findings suggest that while artificial intelligence, particularly machine learning, offers promising solutions, there are significant challenges related to data availability, algorithmic complexity, and real-time application. This review contributes to the field by providing a comprehensive analysis of current AI-driven methods and proposing future research directions to enhance zero-day attack detection.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2024.3455410