UNSW_NB15
Feature coded UNSW_NB15 intrusion detection data.
All categorical features have been converted to numerical values for neural network and SVM processing. First zip is only the csv files and second zip includes the .arff files for weka. For more information on the feature coding process refer to http://scikit-learn.org/stable/modules/preprocessing.html#encoding-categorical-features
Please reference this github for any usage within your research and the conference paper. Original website and author work here: https://www.unsw.adfa.edu.au/australian-centre-for-cyber-security/cybersecurity/ADFA-NB15-Datasets/ Any usage require you to cite the following papers:
Moustafa, Nour, and Jill Slay. "UNSW-NB15: a comprehensive data set for network intrusion detection systems (UNSW-NB15 network data set)."Military Communications and Information Systems Conference (MilCIS), 2015. IEEE, 2015.
Moustafa, Nour, and Jill Slay. "The evaluation of Network Anomaly Detection Systems: Statistical analysis of the UNSW-NB15 data set and the comparison with the KDD99 data set." Information Security Journal: A Global Perspective (2016): 1-14.
Botes, F., Leenen, L. and De La Harpe, R. (2017). Ant Colony Induced Decision Trees for Intrusion Detection. In: 16th European Conference on Cyber Warfare and Security. ACPI (June 12, 2017), pp.74-83.
@article{botes2017ant,
title={Ant colony induced decision trees for intrusion detection},
author={Botes, FH and Leenen, Louise and De La Harpe, R},
year={2017},
publisher={Academic Publishing}
}