Remember the Good, Forget the Bad, do it Fast Continuous Learning over Streaming Data
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F18%3A00327014" target="_blank" >RIV/68407700:21230/18:00327014 - isvavai.cz</a>
Result on the web
<a href="https://hal.inria.fr/hal-01952211" target="_blank" >https://hal.inria.fr/hal-01952211</a>
DOI - Digital Object Identifier
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Alternative languages
Result language
angličtina
Original language name
Remember the Good, Forget the Bad, do it Fast Continuous Learning over Streaming Data
Original language description
Continuous, dynamic and short-term learning is an effective learning strategy when operating in very fast and dynamic environments, where concept drift constantly occurs. 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. Learning takes place by processing a sample at a time, inspecting it only once, and as such, using a limited amount of memory; stream approaches work in a limited amount of time, and have the advantage of performing a prediction 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 environment such as the Internet. We consider adaptive learning algorithms for the analysis of continuously evolving network data streams, using a dynamic, variable length system memory which automatically adapts 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 continuously realize high detection accuracy over dynamic network data streams.
Czech name
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Czech description
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Classification
Type
O - Miscellaneous
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