Novel context-aware network traffic classification based on a machine learning approach

Novel context-aware network traffic classification based on a machine learning approach

Citation Author(s):
Ahmed Saeed, Mario Kolberg
Submitted by:
Ahmed Saeed
Last updated:
Thu, 11/08/2018 - 10:34
DOI:
10.21227/xa1c-bw69
Data Format:
License:
Dataset Views:
251
Rating:
0
0 ratings - Please login to submit your rating.
Share / Embed Cite
Abstract: 

The dataset was constructed by capturing real-time background traffic of 9 applications. The 9 applications represent different types of network behaviour in the background, for high level of network

interaction; we have considered video and voice calls of Skype and Google Hangouts. For the varied level of interactions Facebook and Gmail been chosen, for Gmail,  emails were received at random instances. And tagged posts were received at random instances for Facebook as updates. NSS and NSC chosen to represent all applications with lower degree of interaction, these applications mostly are offline, the interaction occurred only during fetching advertisements. Finally, to represent applications with audio buffering capability XiiaLive internet radio application considered, we chose a random station 128kbps stream. The dataset has been labelled in accordance to the level of interactivity in the background of each application. All inputs of applications with high and constant level of background interactivity are labelled as high. Inputs of applications of varied level of background interactivity were labelled as varied. Low was the label for the inputs of applications with low level of interactivity, and we have labelled the samples of XiiaLive internet radio app with output class buffer.

The dataset with full number of six highly correlated features named as Dataset 1.

Instructions: 

to convert the file into weka readable format you can add the following in the first line of the txt document:

 

@RELATION filelist.weka.allclass.csv

Dataset Files

You must be an IEEE Dataport Subscriber to access these files. Login or subscribe now. Sign up to be a Beta Tester and receive a coupon code for a free subscription to IEEE DataPort!

Thank you for rating this dataset!

Please share additional details of your rating with the IEEE DataPort community by adding a comment.

Embed this dataset on another website

Copy and paste the HTML code below to embed your dataset:

Share via email or social media

Click the buttons below:

facebooktwittermailshare
[1] Ahmed Saeed, Mario Kolberg, "Novel context-aware network traffic classification based on a machine learning approach", IEEE Dataport, 2018. [Online]. Available: http://dx.doi.org/10.21227/xa1c-bw69. Accessed: Apr. 02, 2020.
@data{xa1c-bw69-18,
doi = {10.21227/xa1c-bw69},
url = {http://dx.doi.org/10.21227/xa1c-bw69},
author = {Ahmed Saeed; Mario Kolberg },
publisher = {IEEE Dataport},
title = {Novel context-aware network traffic classification based on a machine learning approach},
year = {2018} }
TY - DATA
T1 - Novel context-aware network traffic classification based on a machine learning approach
AU - Ahmed Saeed; Mario Kolberg
PY - 2018
PB - IEEE Dataport
UR - 10.21227/xa1c-bw69
ER -
Ahmed Saeed, Mario Kolberg. (2018). Novel context-aware network traffic classification based on a machine learning approach. IEEE Dataport. http://dx.doi.org/10.21227/xa1c-bw69
Ahmed Saeed, Mario Kolberg, 2018. Novel context-aware network traffic classification based on a machine learning approach. Available at: http://dx.doi.org/10.21227/xa1c-bw69.
Ahmed Saeed, Mario Kolberg. (2018). "Novel context-aware network traffic classification based on a machine learning approach." Web.
1. Ahmed Saeed, Mario Kolberg. Novel context-aware network traffic classification based on a machine learning approach [Internet]. IEEE Dataport; 2018. Available from : http://dx.doi.org/10.21227/xa1c-bw69
Ahmed Saeed, Mario Kolberg. "Novel context-aware network traffic classification based on a machine learning approach." doi: 10.21227/xa1c-bw69