Publication detail

Traffic Similarity Observation Using a Genetic Algorithm and Clustering

OUJEZSKÝ, V. HORVÁTH, T.

Original Title

Traffic Similarity Observation Using a Genetic Algorithm and Clustering

Type

journal article in Web of Science

Language

English

Original Abstract

This article presents a technique of traffic similarity observation based on the statistical method of survival analysis by using a genetic algorithm. The basis comes from the k-means clustering algorithm. The observed traffic is collected from different network sources by using a NetFlow collector. The purpose of this technique is to propose a process of finding spread malicious traffic, e.g., ransomware, and considers the possibility of implementing a genetic-based algorithm. In our solution, a chromosome is created from clustering k-means centers, and the Davies–Bouldin validity index is used as the second fitness value in the solution.

Keywords

Clustering algorithms, Evolutionary computation, IP networks, Information security, Programming.

Authors

OUJEZSKÝ, V.; HORVÁTH, T.

Released

11. 11. 2018

Publisher

MDPI

Location

Switzerland

ISBN

2227-7080

Periodical

Technologies - MDPI

Year of study

6

Number

4

State

Swiss Confederation

Pages from

1

Pages to

10

Pages count

10

URL

https://www.mdpi.com/2227-7080/6/4/103

Full text in the Digital Library

http://hdl.handle.net/11012/137212

BibTex

@article{BUT138952,
  author="Václav {Oujezský} and Tomáš {Horváth}",
  title="Traffic Similarity Observation Using a Genetic Algorithm and Clustering
",
  journal="Technologies - MDPI",
  year="2018",
  volume="6",
  number="4",
  pages="1--10",
  doi="10.3390/technologies6040103",
  issn="2227-7080",
  url="https://www.mdpi.com/2227-7080/6/4/103"
}