Should I (re)Learn or Should I Go(on)?: Stream Machine Learning for Adaptive Defense against Network Attacks
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F19%3A00338613" target="_blank" >RIV/68407700:21230/19:00338613 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.1145/3338468.3356829" target="_blank" >https://doi.org/10.1145/3338468.3356829</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1145/3338468.3356829" target="_blank" >10.1145/3338468.3356829</a>
Alternative languages
Result language
angličtina
Original language name
Should I (re)Learn or Should I Go(on)?: Stream Machine Learning for Adaptive Defense against Network Attacks
Original language description
Continuous, dynamic and short-term learning is an effective learning strategy when operating in dynamic and adversarial environments, where concept drift constantly occurs and attacks rapidly change over time. In an on-line, stream learning model, data arrives as a stream of sequentially ordered samples, and older data is no longer available to revise earlier suboptimal modeling decisions as the fresh data arrives. Stream approaches work in a limited amount of time, and have the advantage to perform predictions at any point in time during the stream. We focus on a particularly challenging problem, that of continually learning detection models capable to recognize cyber-attacks and system intrusions in a highly dynamic and adversarial environment such as the open Internet. We consider adaptive learning algorithms for the analysis of continuously evolving network data streams, using (dynamic) sliding windows -- representing the system memory, to periodically re-learn, automatically adapting to concept drifts in the underlying data. By continuously learning and detecting concept drifts to adapt memory length, we show that adaptive learning algorithms can realize high detection accuracy of evolving network attacks over dynamic network data streams.
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
International Conference on Software Engineering
ISBN
978-1-4503-6828-5
ISSN
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e-ISSN
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Number of pages
10
Pages from-to
79-88
Publisher name
ACM
Place of publication
New York
Event location
London
Event date
Nov 11, 2019
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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