Exploratory Data Analysis of Network Traffic


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.

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