Por, Lip Yee and Dai, Zhen and Leem, Siew Juan and Chen, Yi and Yang, Jing and Binbeshr, Farid and Yuen Phan, Koo and Soon Ku, Chin (2024) A Systematic Literature Review on AI-Based Methods and Challenges in Detecting Zero-Day Attacks. IEEE Access, 12. pp. 144150-144163. ISSN 2169-3536, DOI https://doi.org/10.1109/ACCESS.2024.3455410.
Full text not available from this repository.Abstract
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.
| Item Type: | Article |
|---|---|
| Funders: | KW IPPP (Research Maintenance Fee) Individual/Centre/Group at Universiti Malaya, Malaysia (RMF1506-2021) |
| Uncontrolled Keywords: | Artificial intelligence; Databases; Intrusion detection; Systematics; Search problems; Object recognition; Anomaly detection; Zero-day attack; CrowdStrike; intrusion detection; anomaly detection; machine learning; artificial intelligence; cybersecurity |
| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science |
| Divisions: | Faculty of Computer Science & Information Technology |
| Depositing User: | Ms. Juhaida Abd Rahim |
| Date Deposited: | 28 Nov 2024 05:16 |
| Last Modified: | 28 Nov 2024 05:16 |
| URI: | http://eprints.um.edu.my/id/eprint/47128 |
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