Optimizing Network Traffic Routing Using Firewall Logs and Machine Learning: A Comprehensive Approach with the ELK Stack

The chapter addresses the escalating challenges faced by network security administrators due to the increasing sophistication of cyberattacks and the growing complexity of enterprise networks. Traditional firewall management practices, characterized by manual configuration, static rules, and the lack of real-time analysis, are shown to be inadequate in addressing these modern threats. The study proposes a novel approach that leverages machine learning to analyze historical network traffic data, enabling the development of adaptive and efficient firewall management systems. By integrating the ELK Stack for log analysis and visualization, the chapter demonstrates how machine learning models can predict firewall actions based on network traffic features, leading to more proactive threat detection and reduced network disruptions. The methodology involves the use of decision trees and convolutional neural networks, each evaluated for their accuracy, execution time, and explainability. The results highlight the potential of these models to automate rule management, enhance threat detection, and optimize network performance. The chapter concludes by discussing the implications of these findings for the future of network security, emphasizing the need for intelligent firewall systems that can adapt to evolving threats and ensure robust network security.