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Open AccessArticle

Traffic Similarity Observation Using a Genetic Algorithm and Clustering

Department of Telecommunication, Brno University of Technology, 616 00 Brno, Czech Republic
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Technologies 2018, 6(4), 103; https://doi.org/10.3390/technologies6040103
Received: 27 October 2018 / Revised: 8 November 2018 / Accepted: 9 November 2018 / Published: 11 November 2018
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. View Full-Text
Keywords: clustering algorithms; evolutionary computation; IP networks; information security; programming clustering algorithms; evolutionary computation; IP networks; information security; programming
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Oujezsky, V.; Horvath, T. Traffic Similarity Observation Using a Genetic Algorithm and Clustering. Technologies 2018, 6, 103.

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Technologies, EISSN 2227-7080, Published by MDPI AG
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