A2 Review article, Literature review, Systematic review
Artificial Intelligence for Cybersecurity : A Systematic Mapping of Literature (2020)

Wiafe, I., Koranteng, F. N., Obeng, E. N., Assyne, N., Wiafe, A., & Gulliver, S. R. (2020). Artificial Intelligence for Cybersecurity : A Systematic Mapping of Literature. IEEE Access, 8, 146598-146612. https://doi.org/10.1109/ACCESS.2020.3013145

JYU authors or editors

Publication details

All authors or editors: Wiafe, Isaac; Koranteng, Felix N.; Obeng, Emmanuel N.; Assyne, Nana; Wiafe, Abigail; Gulliver, Stephen R.

Journal or series: IEEE Access

eISSN: 2169-3536

Publication year: 2020

Volume: 8

Pages range: 146598-146612

Publisher: IEEE

Publication country: United States

Publication language: English

DOI: https://doi.org/10.1109/ACCESS.2020.3013145

Publication open access: Openly available

Publication channel open access: Open Access channel

Publication is parallel published (JYX): https://jyx.jyu.fi/handle/123456789/73706


Due to the ever-increasing complexities in cybercrimes, there is the need for cybersecurity methods to be more robust and intelligent. This will make defense mechanisms to be capable of making real-time decisions that can effectively respond to sophisticated attacks. To support this, both researchers and practitioners need to be familiar with current methods of ensuring cybersecurity (CyberSec). In particular, the use of artificial intelligence for combating cybercrimes. However, there is lack of summaries on artificial intelligent methods for combating cybercrimes. To address this knowledge gap, this study sampled 131 articles from two main scholarly databases (ACM digital library and IEEE Xplore). Using a systematic mapping, the articles were analyzed using quantitative and qualitative methods. It was observed that artificial intelligent methods have made remarkable contributions to combating cybercrimes with significant improvement in intrusion detection systems. It was also observed that there is a reduction in computational complexity, model training times and false alarms. However, there is a significant skewness within the domain. Most studies have focused on intrusion detection and prevention systems, and the most dominant technique used was support vector machines. The findings also revealed that majority of the studies were published in two journal outlets. It is therefore suggested that to enhance research in artificial intelligence for CyberSec, researchers need to adopt newer techniques and also publish in other related outlets.

Keywords: netcrime; cyber crime; cyber security; data security; machine learning; artificial intelligence; literature surveys; systematic reviews

Free keywords: machine learning; protocols; computer crime; artificial intelligence; cybersecurity; information security; systematic review

Contributing organizations

Ministry reporting: Yes

Reporting Year: 2020

JUFO rating: 2

Last updated on 2021-07-07 at 21:33