Authors :
G. S. Nagaraja; Kruthi P.R; Shivani Deshpande
Volume/Issue :
Volume 7 - 2022, Issue 7 - July
Google Scholar :
https://bit.ly/3IIfn9N
Scribd :
https://bit.ly/3QUMmfI
DOI :
https://doi.org/10.5281/zenodo.7025473
Abstract :
We cannot deny that internet and many
applications running on internet are growing at an
exponential rate. It has become more important for
network administrators to have an understanding of the
different types of network traffic.
This paper reviews the topic of exploratory data
analysis of captured network traffic. There has been an
exponential rise in the use of web apps such as social
media sites, e-commerce, video streaming services, blogs,
e-banking lately. These application has now become the
day-to-day mode of communication all over the world
and its importance is ever growing.
In this paper, we explore and investigate the data
being transmitted by 75 apps and segregate the users of
these apps based on network usage and try to determine
the most used apps. The output of this exploratory data
analysis can be applied in various fields.
Keywords :
Data preprocessing, data mining, k-means clustering.
We cannot deny that internet and many
applications running on internet are growing at an
exponential rate. It has become more important for
network administrators to have an understanding of the
different types of network traffic.
This paper reviews the topic of exploratory data
analysis of captured network traffic. There has been an
exponential rise in the use of web apps such as social
media sites, e-commerce, video streaming services, blogs,
e-banking lately. These application has now become the
day-to-day mode of communication all over the world
and its importance is ever growing.
In this paper, we explore and investigate the data
being transmitted by 75 apps and segregate the users of
these apps based on network usage and try to determine
the most used apps. The output of this exploratory data
analysis can be applied in various fields.
Keywords :
Data preprocessing, data mining, k-means clustering.
