Network Security Traffic Analysis Platform - Design and Validation

Real-time traffic management and control have become necessary in today's networks due to their complexity and cybersecurity risks. With the increase in Internet use, threats are more prevalent and require real-time detection and analysis to prevent network intrusions. As the number of data flow increases, the number and the types of attacks increase, which makes detecting intrusions challenging. Therefore, over the last years, many researchers have focused on different ways to detect and more importantly prevent these intrusions. In this work, we describe the design and evaluation of a network security traffic analysis platform (NSTAP) that collects, searches, and analyzes traffic data in real time in order to filter out malicious flows. Through charts, tables, histograms, and other visualization methods, we demonstrate that the platform can produce powerful and useful insights with simple time-domain analytics of large data volumes. This work is intended to be the foundation for more automation tools based on machine learning.