Adaptive network security through stream machine learning
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F18%3A00325944" target="_blank" >RIV/68407700:21230/18:00325944 - isvavai.cz</a>
Result on the web
<a href="https://dl.acm.org/citation.cfm?doid=3234200.3234246" target="_blank" >https://dl.acm.org/citation.cfm?doid=3234200.3234246</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1145/3234200.3234246" target="_blank" >10.1145/3234200.3234246</a>
Alternative languages
Result language
angličtina
Original language name
Adaptive network security through stream machine learning
Original language description
Stream Machine Learning is rapidly gaining popularity within the network monitoring community as the big data produced by network devices and end-user terminals goes beyond the memory constraints of standard monitoring equipment. We consider a stream-based machine learning approach to network security, conceiving adaptive machine learning algorithms for the analysis of continuously evolving network data streams. Using a sliding-window adaptive-size approach, we show that adaptive random forests models are able to keep up with important concept drifts in the underlying network data streams, by keeping high accuracy with continuous re-training at concept drift detection times.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
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Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2018
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
SIGCOMM '18 Proceedings of the ACM SIGCOMM 2018 Conference on Posters and Demos
ISBN
978-1-4503-5915-3
ISSN
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e-ISSN
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Number of pages
2
Pages from-to
4-5
Publisher name
ACM
Place of publication
New York
Event location
Budapest
Event date
Aug 20, 2018
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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