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Detecting Advanced Network Threats Using a Similarity Search

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ČERMÁK, Milan and Pavel ČELEDA. Detecting Advanced Network Threats Using a Similarity Search. In Management and Security in the Age of Hyperconnectivity. 9701st ed. Munich, Germany: Springer International Publishing, 2016. p. 137-141. ISBN 978-3-319-39813-6.
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Basic information
Original name Detecting Advanced Network Threats Using a Similarity Search
Name in Czech Detekce pokročilých síťových útoků pomocí podobnostního vyhledávání
Authors ČERMÁK, Milan (203 Czech Republic, guarantor, belonging to the institution) and Pavel ČELEDA (203 Czech Republic, belonging to the institution).
Edition 9701. vyd. Munich, Germany, Management and Security in the Age of Hyperconnectivity, p. 137-141, 5 pp. 2016.
Publisher Springer International Publishing
Other information
Original language English
Type of outcome Proceedings paper
Field of Study Informatics
Country of publisher Germany
Confidentiality degree is not subject to a state or trade secret
Publication form printed version "print"
WWW URL
RIV identification code RIV/00216224:14610/16:00087690
Organization Ústav výpočetní techniky - Repository
ISBN 978-3-319-39813-6
ISSN 0302-9743
UT WoS 000389804200014
Keywords (in Czech) podobnostní vyhledávání, síťová data, klasifikace, síťové hrozby
Keywords in English similarity search; network data; classification; network threats
Links VI20162019029, research and development project.
Changed by Changed by: RNDr. Daniel Jakubík, učo 139797. Changed: 2/9/2020 22:22.
Abstract
In this paper, we propose a novel approach for the detection of advanced network threats. We combine knowledge-based detections with similarity search techniques commonly utilized for automated image annotation. This unique combination could provide effective detection of common network anomalies together with their unknown variants. In addition, it offers a similar approach to network data analysis as a security analyst does. Our research is focused on understanding the similarity of anomalies in network traffic and their representation within complex behaviour patterns. This will lead to a proposal of a system for the realtime analysis of network data based on similarity. This goal should be achieved within a period of three years as a part of a PhD thesis.
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2016-AIMS-detecting-network-threats-similarity-search-presentation.pdf Licence Creative Commons 2/9/2020

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paper.pdf Licence Creative Commons  File version 25/3/2017

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  • a concrete person Mgr. Jolana Surýnková, uco 220973
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presentation.pdf Licence Creative Commons 25/6/2016

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