A Review of Neural Networks for Enhanced User Entity Behavior Analytics in Cybersecurity: Addressing the Challenge of Vanishing Gradient

Akampurira Paul, Bashir Olaniyi Sadiq, Dahiru Buhari, Maninti Venkateswarlu, Atuhe Aarone and Mugisha Brian
Additional contact information
Akampurira Paul: Kampala International University, Uganda
Bashir Olaniyi Sadiq: Kampala International University, Uganda
Dahiru Buhari: Kampala International University, Uganda
Maninti Venkateswarlu: Kampala International University, Uganda Mbarara University of Science and Technology
Atuhe Aarone: Kampala International University, Uganda Mbarara University of Science and Technology
Mugisha Brian: Kampala International University, Uganda Mbarara University of Science and Technology

International Journal of Research and Innovation in Applied Science, 2024, vol. 9, issue 12, 154-172

Abstract: In the dynamic realm of cybersecurity, User Entity Behavior Analytics (UEBA) emerges as a pivotal tool, employing advanced data analytics and machine learning to scrutinize user and entity activities, thereby detecting potential insider threats. Recurrent neural networks (RNNs) are particularly notable in this context for their ability to identify complex temporal patterns and strengthen threat detection systems. The vanishing gradient issue, in particular, presents difficulties for efficient model training and convergence, hence the use of RNNs in UEBA is not without its difficulties. This explorative article delves into the nuances of the vanishing gradient issue within RNN architectures in the context of UEBA. By dissecting the challenge and exploring potential solutions, we aim to provide readers with a comprehensive understanding of the complexities involved and pave the way for future research directions in optimizing RNNs for enhanced cybersecurity applications. Our analysis reveals important gaps in the application of neural networks for insider threat prediction and the advancement of behavior analytics systems. We give important insights into the challenges of handling the vanishing gradient, and we conclude by recommending neural network optimization methods for behavior analytics and cybersecurity to scholars, practitioners, and organizations.

Date: 2024
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.rsisinternational.org/journals/ijrias/ ... issue-12/154-172.pdf (application/pdf)
https://rsisinternational.org/journals/ijrias/arti ... -vanishing-gradient/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:bjf:journl:v:9:y:2024:i:12:p:154-172

Access Statistics for this article

International Journal of Research and Innovation in Applied Science is currently edited by Dr. Renu Malsaria

More articles in International Journal of Research and Innovation in Applied Science from International Journal of Research and Innovation in Applied Science (IJRIAS)
Bibliographic data for series maintained by Dr. Renu Malsaria ().