Malicious Traffic Classification in a UNSW-NB15 Dataset by Using Tomeklinks and ClusBUS 


Vol. 46,  No. 11, pp. 1896-1899, Nov.  2021
10.7840/kics.2021.46.11.1896


PDF
  Abstract

The UNSW-NB15 datasets have traffic information consisting of 42 features for 9 attack types and normal type. We classified ‘Exploits", ‘Fuzzers", ‘Generic", and ‘Normal" data types in the datasets. It was confirmed that there were data overlap and imbalance problems in Fuzzers and Normal types. We showed performance enhancement by using Tomeklinks and ClusBUS techniques to solve data overlap and imbalance problems.

  Statistics
Cumulative Counts from November, 2022
Multiple requests among the same browser session are counted as one view. If you mouse over a chart, the values of data points will be shown.


  Cite this article

[IEEE Style]

P. Yoon and G. Hwang, "Malicious Traffic Classification in a UNSW-NB15 Dataset by Using Tomeklinks and ClusBUS," The Journal of Korean Institute of Communications and Information Sciences, vol. 46, no. 11, pp. 1896-1899, 2021. DOI: 10.7840/kics.2021.46.11.1896.

[ACM Style]

Pil-Do Yoon and Gyung-Ho Hwang. 2021. Malicious Traffic Classification in a UNSW-NB15 Dataset by Using Tomeklinks and ClusBUS. The Journal of Korean Institute of Communications and Information Sciences, 46, 11, (2021), 1896-1899. DOI: 10.7840/kics.2021.46.11.1896.

[KICS Style]

Pil-Do Yoon and Gyung-Ho Hwang, "Malicious Traffic Classification in a UNSW-NB15 Dataset by Using Tomeklinks and ClusBUS," The Journal of Korean Institute of Communications and Information Sciences, vol. 46, no. 11, pp. 1896-1899, 11. 2021. (https://doi.org/10.7840/kics.2021.46.11.1896)

Search
(IN TITLE, AUTHOR, ABSTRACT,KEYWORDS)

Advanced Search ▼

POPULAR KEYWORDS
(TOP 10 KEYWORDS)

Recent Publications
(LAST 3 YEARS)






pISSN : 1226 - 4717
eISSN : 2287 - 3880