A Comparative Analysis of Deep Learning Approaches for Enhancing Security in Web Applications

This paper provides a comparative analysis of deep learning algorithms for detecting web attacks and code vulnerability, A review of the literature highlights the methodologies, datasets used, achieved accuracies of these models and their limitations. By understanding web attacks and leveraging advanced technologies, it is possible to enhance the security and protection of digital assets. A notable observation is the underutilization of resources to protect JavaScript, a widely used programming language on the internet. To address this gap, our research prioritizes improving JavaScript’s security. We aim to develop a system that improve web application protection, ensuring a safer online environment for users.