LEVERAGING MACHINE LEARNING FOR DYNAMIC WEB TRAFFIC ANALYSIS: A TECHNICAL DEEP DIVE

Abstract

This comprehensive technical article explores the integration of machine learning in web traffic analysis and security, focusing on behavioral analysis and dynamic policy updates for Web Application Firewalls (WAF). It examines the establishment of behavioral baselines, implementation of machine learning models, and real-time adaptation mechanisms in cybersecurity. The article addresses the challenges of scalability and accuracy enhancement while highlighting the crucial role of feature engineering and policy optimization in maintaining robust security measures. It investigates how organizations can leverage machine learning algorithms to detect and respond to emerging threats through automated rule generation and intelligent pattern matching. Furthermore, the article explores the future directions of ML-based security solutions, including advanced feature extraction techniques, sophisticated fingerprinting methods, and the integration of deep learning models for complex pattern recognition. It emphasizes the importance of balancing security effectiveness with operational efficiency while maintaining optimal protection levels and minimal impact on application performance.