Statistical evaluation of network traffic variations using hypothesis testing methods

Abstract

This paper presents a statistical approach for detecting and analyzing variations in network traffic using hypothesis testing methods such as the z-test, t-test, and chi-square test. The study aims to determine whether changes in traffic behavior are statistically significant and could indicate potential cyberattacks or anomalies. The proposed approach provides an interpretable, mathematically grounded framework fo r network monitoring without relying on complex machine learning algorithms. Experimental results demonstrate that statistical hypothesis testing can effectively differentiate normal traffic from abnormal or attack traffic, thereby contributing to improved cybersecurity analysis.