Leveraging AI for Zero-Day Attack Detection
Challenges and Future Directions
Keywords:
zero-day attacks, artificial intelligence, cybersecurity, machine learningAbstract
Zero-day attacks pose a significant threat to cybersecurity, exploiting vulnerabilities that are unknown to software vendors and security professionals. These attacks can lead to severe financial and reputational damage to organizations, making early detection critical. Artificial intelligence (AI) offers promising solutions for identifying these threats through advanced pattern recognition and anomaly detection techniques. This paper examines the application of AI models in detecting zero-day attacks, highlighting the unique challenges faced in their early detection. It further explores future research directions aimed at enhancing detection accuracy, including the integration of machine learning techniques, improved data gathering methods, and the development of more robust algorithms. The findings underscore the potential of AI to transform zero-day attack detection, but also emphasize the need for ongoing research to address existing limitations.
Downloads
Downloads
Published
Issue
Section
License
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
License Terms
Ownership and Licensing:
Authors of this research paper submitted to the journal owned and operated by The Science Brigade Group retain the copyright of their work while granting the journal certain rights. Authors maintain ownership of the copyright and have granted the journal a right of first publication. Simultaneously, authors agreed to license their research papers under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) License.
License Permissions:
Under the CC BY-NC-SA 4.0 License, others are permitted to share and adapt the work, as long as proper attribution is given to the authors and acknowledgement is made of the initial publication in the Journal. This license allows for the broad dissemination and utilization of research papers.
Additional Distribution Arrangements:
Authors are free to enter into separate contractual arrangements for the non-exclusive distribution of the journal's published version of the work. This may include posting the work to institutional repositories, publishing it in journals or books, or other forms of dissemination. In such cases, authors are requested to acknowledge the initial publication of the work in this Journal.
Online Posting:
Authors are encouraged to share their work online, including in institutional repositories, disciplinary repositories, or on their personal websites. This permission applies both prior to and during the submission process to the Journal. Online sharing enhances the visibility and accessibility of the research papers.
Responsibility and Liability:
Authors are responsible for ensuring that their research papers do not infringe upon the copyright, privacy, or other rights of any third party. The Science Brigade Publishers disclaim any liability or responsibility for any copyright infringement or violation of third-party rights in the research papers.
