Denial of Service Attack: Analysis of Network Traffic Anormaly using Queuing Theory

TL;DR

This paper investigates detecting denial-of-service attacks by analyzing network traffic anomalies using queuing theory and system performance parameters, aiming for rapid attack diagnosis.

Contribution

It introduces a novel approach combining queuing theory with performance analysis to detect DOS attacks in real-time network traffic.

Findings

High accuracy in anomaly detection

Effective identification of DOS attack patterns

Potential for real-time network security enhancement

Abstract

Denial-of-service (DOS) attacks increasingly gained reputation over the past few years. As the Internet becomes more ubiquitous, the threat of the denial-of-service attacks becomes more realistic and important for individuals, businesses, governmental organizations, and even countries. There is intensive need to detect an attack in progress as soon as possible. The efficiency of diagnosing the DOS attack using concepts of queuing theory and performance parameter of the system has been investigated in the present work, as the servers definitely have some mechanisms to store and process the requests. Utilizing this concept of queuing theory, the collection of data patterns were generated. With the performance parameter of the system, the analysis of the data pattern had been made to diagnose the network anomaly. Performance analysis and results show the accuracy of the proposed scheme in detecting anomalies.