Calibrated Uncertainty Quantification on Auto-Encoders for Anomaly Detection with Standard Deviation as Metric
Jordan F. Masakuna, D’Jeff K. Nkashama, Arian Soltani, Marc Frappier, Pierre-Martin Tardif, Froduald Kabanza · 2023
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].