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Current trends in AI and ML for cybersecurity: A state-of-the-art survey.

Authors :
Mohamed, Nachaat
Source :
Cogent Engineering. 2023, Vol. 10 Issue 2, p1-30. 30p.
Publication Year :
2023

Abstract

This paper provides a comprehensive survey of the state-of-the-art use of Artificial Intelligence (AI) and Machine Learning (ML) in the field of cybersecurity. The paper illuminates key applications of AI and ML in cybersecurity, while also addressing existing challenges and posing unresolved questions for future research. The paper also emphasizes the ethical and legal implications associated with their implementation. The researchers conducted a thorough survey by reviewing numerous papers and articles from respected sources such as IEEE, ACM, and Springer. Their focus centered on the latest AI and ML breakthroughs in cybersecurity, while also exploring current challenges and open research questions. The results indicate that integrating AI and ML into cybersecurity systems shows great potential for future research and development. Intrusion detection and response, malware detection, and network security are among the most promising applications identified. According to the survey, 45% of organizations have already implemented AI and ML in their cybersecurity systems, while an additional 35% plan to do so. However, 20% of organizations believe that it is not yet the right time for adopting these technologies. Overall, this paper serves as a reliable reference for researchers and practitioners in the field of cybersecurity, offering a comprehensive overview of the use of AI and ML. It not only highlights the potential applications but also addresses the challenges and research gaps. Additionally, the paper raises awareness about the ethical and legal considerations associated with leveraging AI and ML in the cybersecurity domain. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
23311916
Volume :
10
Issue :
2
Database :
Academic Search Index
Journal :
Cogent Engineering
Publication Type :
Academic Journal
Accession number :
174237166
Full Text :
https://doi.org/10.1080/23311916.2023.2272358