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  • Sampling techniques are widely employed in high-speed network traffic monitoring to allow the analysis of high traffic volumes with limited resources. Sampling has measurable negative impact on the accuracy of network anomaly detection methods. In our work, we build an integrated model which puts the sampling into the context of the anomaly detection used in the subsequent processing. Using this model, we show that it is possible to perform very efficient sampling with limited impact on traffic feature distributions, thus minimizing the decrease of anomaly detection efficiency. Specifically, we propose an adaptive, feature-aware statistical sampling technique and compare it both formally and empirically with other known sampling techniques - random flow sampling and selective sampling. We study the impact of these sampling techniques on particular anomaly detection methods used in a network behavior analysis system.
  • Sampling techniques are widely employed in high-speed network traffic monitoring to allow the analysis of high traffic volumes with limited resources. Sampling has measurable negative impact on the accuracy of network anomaly detection methods. In our work, we build an integrated model which puts the sampling into the context of the anomaly detection used in the subsequent processing. Using this model, we show that it is possible to perform very efficient sampling with limited impact on traffic feature distributions, thus minimizing the decrease of anomaly detection efficiency. Specifically, we propose an adaptive, feature-aware statistical sampling technique and compare it both formally and empirically with other known sampling techniques - random flow sampling and selective sampling. We study the impact of these sampling techniques on particular anomaly detection methods used in a network behavior analysis system. (en)
Title
  • Optimizing Flow Sampling for Network Anomaly Detection
  • Optimizing Flow Sampling for Network Anomaly Detection (en)
skos:prefLabel
  • Optimizing Flow Sampling for Network Anomaly Detection
  • Optimizing Flow Sampling for Network Anomaly Detection (en)
skos:notation
  • RIV/68407700:21230/11:00181849!RIV12-MSM-21230___
http://linked.open...avai/predkladatel
http://linked.open...avai/riv/aktivita
http://linked.open...avai/riv/aktivity
  • P(ME10051), P(MEB111008), S
http://linked.open...vai/riv/dodaniDat
http://linked.open...aciTvurceVysledku
http://linked.open.../riv/druhVysledku
http://linked.open...iv/duvernostUdaju
http://linked.open...titaPredkladatele
http://linked.open...dnocenehoVysledku
  • 218798
http://linked.open...ai/riv/idVysledku
  • RIV/68407700:21230/11:00181849
http://linked.open...riv/jazykVysledku
http://linked.open.../riv/klicovaSlova
  • NetFlow; sampling methods; anomaly detection; network traffic (en)
http://linked.open.../riv/klicoveSlovo
http://linked.open...ontrolniKodProRIV
  • [E99FBA2CC6C2]
http://linked.open...v/mistoKonaniAkce
  • Istanbul
http://linked.open...i/riv/mistoVydani
  • Piscataway
http://linked.open...i/riv/nazevZdroje
  • Wireless Communications and Mobile Computing 2011
http://linked.open...in/vavai/riv/obor
http://linked.open...ichTvurcuVysledku
http://linked.open...cetTvurcuVysledku
http://linked.open...vavai/riv/projekt
http://linked.open...UplatneniVysledku
http://linked.open...iv/tvurceVysledku
  • Bartoš, Karel
  • Rehák, Martin
  • Krmíček, V.
http://linked.open...vavai/riv/typAkce
http://linked.open.../riv/zahajeniAkce
number of pages
http://purl.org/ne...btex#hasPublisher
  • IEEE
https://schema.org/isbn
  • 978-1-4244-9539-9
http://localhost/t...ganizacniJednotka
  • 21230
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