NEURAL NETWORKS FOR WEB APPLICATION SECURITY

Abstract

The publication focuses on the application of machine learning to enhance the security of web applications. It examines the limitations of traditional protection methods, such as web application firewalls (WAF), continuous monitoring using SIEM systems, and penetration testing. The principles of neural networks, their classification, and their potential for automating traffic analysis, detecting anomalies, and protecting against zero-day vulnerabilities have been analyzed. The advantages and disadvantages of neural networks are described, their integration with existing tools is justified, and a comparative analysis of modern WAFs with machine learning is conducted.