Continuous and Adaptive Learning over Big Streaming Data for Network Security
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F19%3A00338618" target="_blank" >RIV/68407700:21230/19:00338618 - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.1109/CloudNet47604.2019.9064134" target="_blank" >http://dx.doi.org/10.1109/CloudNet47604.2019.9064134</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/CloudNet47604.2019.9064134" target="_blank" >10.1109/CloudNet47604.2019.9064134</a>
Alternative languages
Result language
angličtina
Original language name
Continuous and Adaptive Learning over Big Streaming Data for Network Security
Original language description
Continuous and adaptive learning is an effective learning approach when dealing with highly dynamic and changing scenarios, where concept drift often happens. In a continuous, stream or adaptive learning setup, new measurements arrive continuously and there are no boundaries for learning, meaning that the learning model has to decide how and when to (re)learn from these new data constantly. We address the problem of adaptive and continual learning for network security, building dynamic models to detect network attacks in real network traffic. The combination of fast and big network measurements data with the re-training paradigm of adaptive learning imposes complex challenges in terms of data processing speed, which we tackle by relying on big data platforms for parallel stream processing. We build and benchmark different adaptive learning models on top of a novel big data analytics platform for network traffic monitoring and analysis tasks, and show that high speed-up computations (as high as x 6) can be achieved by parallelizing off-the-shelf stream learning approaches.
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
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2019
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
IEEE International Conference on Cloud Networking
ISBN
978-1-7281-4832-8
ISSN
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e-ISSN
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Number of pages
4
Pages from-to
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Publisher name
IEEE
Place of publication
Santa Monica
Event location
Coimbra
Event date
Nov 4, 2019
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
000574777100031