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Calibrated Uncertainty Quantification on Auto-Encoders for Anomaly Detection with Standard Deviation as Metric
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  • Jordan F. Masakuna ,
  • D'Jeff K. Nkashama ,
  • Arian Soltani ,
  • Marc Frappier ,
  • Pierre-Martin Tardif ,
  • Froduald Kabanza
Jordan F. Masakuna
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D'Jeff K. Nkashama
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Arian Soltani
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Marc Frappier
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Pierre-Martin Tardif
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Froduald Kabanza
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Abstract

We use the following anomaly detection benchmark data sets:
  • KDDCUP is an old cyber-intrusion detection benchmark data set and conveys some well-known issues such as duplicated data points. It contains simulated military-like data for normal traffics and several types of attacks.
  • NSL-KDD is a revisited version of the KDDCUP data set provided by the Canadian Institute of Cybersecurity.
  • CIC-CSE-IDS2018 is a recent simulated data set containing normal traffics and several types of attacks from a complex network. It is a collaboration-work production between the Canadian Institute of Cybersecurity and the Communication Security Establishment [57].
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