The UNSW-NB15 dataset with binarized features

Binarized version of the UNSW-NB15 dataset, where the original features (a mix of strings, categorical values, floating point values etc) are converted to a bit string of 593 bits. Each value in each feature is either 0 or 1, stored as a uint8 value. The uint8 values are represented as numpy arrays, provided separately for training and test data (same train/test split as the original dataset is used). The final binary value in each sample is the expected output.

Among others, this dataset has been used for quantized neural network research:

Umuroglu, Y., Akhauri, Y., Fraser, N. J., & Blott, M. (2020, August). LogicNets: Co-Designed Neural Networks and Circuits for Extreme-Throughput Applications. In 2020 30th International Conference on Field-Programmable Logic and Applications (FPL) (pp. 291-297). IEEE.

The method for binarization is identical to the one described in 10.5281/zenodo.3258657 :

 "T. Murovič, A. Trost, Massively Parallel Combinational Binary Neural Networks for Edge Processing, Elektrotehniški vestnik, vol. 86, no. 1-2, pp. 47-53, 2019"

The original UNSW-NB15 dataaset is by:

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.