Flow-Based Compromise Detection: Lessons Learned

Rick Hofstede (Corresponding Author), Aiko Pras (Corresponding Author), Anna Sperotto, Gabi Dreo Rodosek

Research output: Contribution to journal › Article › Academic › peer-review

Abstract

Although the aggregated nature of exported flow data provides many advantages in terms of privacy and scalability, flow data may contain artifacts that impair data analysis. In this article, we investigate the differences between flow data analysis in theory and practice — that is, in lab environments and production networks.
LanguageEnglish
Pages82-89
JournalIEEE security & privacy
Volume16
Issue number1
DOIs
Publication statusPublished - Jan 2018

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Data flow analysis
compromise
Scalability
data analysis
privacy
artifact

Keywords

  • Hybride overig

Cite this

Hofstede, Rick ; Pras, Aiko ; Sperotto, Anna ; Dreo Rodosek, Gabi. / Flow-Based Compromise Detection : Lessons Learned. In: IEEE security & privacy. 2018 ; Vol. 16, No. 1. pp. 82-89.
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Hofstede, R, Pras, A, Sperotto, A & Dreo Rodosek, G 2018, 'Flow-Based Compromise Detection: Lessons Learned' IEEE security & privacy, vol. 16, no. 1, pp. 82-89. https://doi.org/10.1109/MSP.2018.1331021

Flow-Based Compromise Detection : Lessons Learned. / Hofstede, Rick (Corresponding Author); Pras, Aiko (Corresponding Author); Sperotto, Anna ; Dreo Rodosek, Gabi.

In: IEEE security & privacy, Vol. 16, No. 1, 01.2018, p. 82-89.

Research output: Contribution to journal › Article › Academic › peer-review

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AB - Although the aggregated nature of exported flow data provides many advantages in terms of privacy and scalability, flow data may contain artifacts that impair data analysis. In this article, we investigate the differences between flow data analysis in theory and practice — that is, in lab environments and production networks.

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Hofstede R, Pras A, Sperotto A, Dreo Rodosek G. Flow-Based Compromise Detection: Lessons Learned. IEEE security & privacy. 2018 Jan;16(1):82-89. https://doi.org/10.1109/MSP.2018.1331021

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