Traffic Similarity Observation Using a Genetic Algorithm and Clustering

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F18%3APU124532" target="_blank" >RIV/00216305:26220/18:PU124532 - isvavai.cz</a>
Result on the web
<a href="https://www.mdpi.com/2227-7080/6/4/103" target="_blank" >https://www.mdpi.com/2227-7080/6/4/103</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.3390/technologies6040103" target="_blank" >10.3390/technologies6040103</a>

Result language
angličtina
Original language name
Traffic Similarity Observation Using a Genetic Algorithm and Clustering
Original language description
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.
Czech name
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Czech description
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Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
—
OECD FORD branch
20203 - Telecommunications

Project
Result was created during the realization of more than one project. More information in the Projects tab.
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year
2018
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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