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Network Security Traffic Analysis Platform - Design and Validation

Published

Author(s)

Zineb Maasaoui, Anfal Hathah, Hasnae Bilil, Van Sy Mai, Abdella Battou, ahmed lbath, Veton KEPUSKA

Abstract

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 present and require real-time detection and analysis to prevent intrusions. As the number of data flows increases, the number and the types of attacks increase, which makes detecting intrusions challenging. Over the last years, many researchers have focused on different ways to detect intrusions in different systems. In this work, we describe the design and evaluation of a network security traffic analysis platform (NSTAP) to collect, view, search and analyze traffic data in real-time. Through charts, tables, histograms and other visualization methods, we demonstrate its power and usefulness with results obtained with simple time analytics of large data volumes. This work is intended to be the foundation for machine learning based automation tools.
Proceedings Title
Main Conference / Symposium
Conference Dates
December 5-7, 2022
Conference Location
Abu-Dhabi, AE
Conference Title
19th ACS/IEEE International Conference on Computer Systems and Applications AICCSA 2022

Keywords

Network, Security, Intrusion Detection, Traffic management, Machine learning

Citation

Maasaoui, Z. , Hathah, A. , Bilil, H. , Mai, V. , Battou, A. , Lbath, A. and KEPUSKA, V. (2023), Network Security Traffic Analysis Platform - Design and Validation, Main Conference / Symposium, Abu-Dhabi, AE, [online], https://doi.org/10.1109/AICCSA56895.2022.100178622.10017862, https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=932794 (Accessed May 29, 2024)

Issues

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Created January 20, 2023, Updated January 18, 2024