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

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.