Přehled o publikaci
2016
Detecting Advanced Network Threats Using a Similarity Search
ČERMÁK, Milan and Pavel ČELEDABasic 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
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"
References:
RIV identification code
RIV/00216224:14610/16:00087690
Organization
Ústav výpočetní techniky – Repository – Repository
ISBN
978-3-319-39813-6
ISSN
UT WoS
000389804200014
EID Scopus
2-s2.0-84976643067
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: 2/9/2020 22:22, RNDr. Daniel Jakubík
Abstract
V originále
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.