UNAVOIDS: Unsupervised and Nonparametric Approach for Visualizing Outliers and Invariant Detection Scoring
aggregate.py
: this script aggregates the scores outputted by expriments.py
experiments.py
: This script runs UNAVIODS and some comparison algorithms across a grid of parameters on a window specified by a command line argument.
genPrevelanceArrs.py
: Thus script creates Pis_\[i\].npy
for each i, where i corresponds to the index of the i^th prevelance in the array Pis each Pis_\[i\].npy
contains a n' < n
length vector of indicies, such that the prevelance of outliers in the subset pointed to by these indicies corresponds to the ith value of Pis.
genSimulatedDataSet.py
: This script generates a simulated dataset
genSimulationNCDFs.py
: This script finds the NCDFs for simulated data using different norms and numbers of features, then creates CSV files for LaTeX to plot the NCDFs
loadAndCleanDoH.py
: This file loads and cleans the data from the CIRA-CIC-DoHBrw-2020
data set
loadAndCleanIDS.py
: This file loads and cleans the data from the CICIDS2017
data set
txtToLatex.py
: This file loads the TXT file outputted by aggregate.py
and creates several CSV's saved in \[outfolder\]
. These can be used by our LaTeX code to create latex plots.
txtToLatex.py
: the unavoids outlier detection and visualization library. All functions have a docstring describe their use.
main.py
: basic usuage example of the unavioids library
W. A. Yousef, I. Traoré and W. Briguglio, "UN-AVOIDS: Unsupervised and Nonparametric Approach for Visualizing Outliers and Invariant Detection Scoring," in IEEE Transactions on Information Forensics and Security, vol. 16, pp. 5195-5210, 2021, doi: 10.1109/TIFS.2021.3125608.
https://codeocean.com/capsule/2122579/tree/v1
Please, cite this work as:
@ARTICLE{Yousef2021UNAVOIDS,
author={Yousef, Waleed A. and Traoré, Issa and Briguglio, William},
journal={IEEE Transactions on Information Forensics and Security},
title={UN-AVOIDS: Unsupervised and Nonparametric Approach for Visualizing Outliers and Invariant Detection Scoring},
year={2021},
volume={16},
pages={5195-5210},
doi={10.1109/TIFS.2021.3125608}}