This repository constains the set of scripts used on the paper "Automated Selection of Network Traffic Models through Bayesian and Akaike Information Criteria", and a tutorial of how to reproduce the experiments with the same or new pcap files.

Sumary:

1. Dependencies
2. Setup Enviroment
3. Tutorial
4. Files documentation

To run these scripts, the follow dependencies are required:

sudo apt install python3-pip
pip install -U matplotlib --user
pip3 install numpy
sudo apt-get install python3-tk

To install and setup octave, use the follow commands:

# add repository
sudo add-apt-repository ppa:octave/stable
sudo apt-get update
# install Octave
sudo apt-get install octave
sudo apt-get install liboctave-dev
# other packages dependencies
sudo apt-get install gnuplot epstool transfig pstoedit

Install statistic packeges on Octave to run the simulations. Run the follow command to start Octave CLI:

octave-cli

Inside Octave CLI, run the folloe commands:

octave> pkg -forge install io
octave> pkg -forge install statistics

After running these commands, a directory on home called .config/octave will appear. But it may have some ownership/access problems. To solve it, run this command on Shell terminal:

sudo chown $USER ~/.config/octave/qt-settings

The pcap files used on these experiments are provided on the repository https://github.com/AndersonPaschoalon/Pcaps. To run the tests, we recomend clonning this repository (or its code-only version) and the Pcap repository side by side:

(root-dir)/
├── aic-bic-paper/
├── Pcaps/

To prepare the You can do this by using the follow commands:

mkdir aic-bic-tests
cd aic-bic-tests
git clone https://github.com/AndersonPaschoalon/Pcaps
git clone git clone https://github.com/AndersonPaschoalon/aic-bic-paper

To generate the pcap files:

./git-setup.sh --merge

After that, to clean-up the local repository (excludign part files), you may execute:

./git-setup.sh --rm

To run the simulations, use run.py. This is a script to automate and simplify the script execution, maintaining the consistency, without having to know inner details. Runing run.py --help we have an example:

./run.py <pcap_file>  <simulation_name>
	Eg.: ./run.py ../pcaps/wireshark-wiki_http.pcap  wombat

The first argument must be the relative path, and the second the name of the simulation. A directory with the simulation nada will be created at plots/ directory. After that, to generate the figures, run:

./plot.py --simulation "plots/<simulation_name>"

Supose you have a pcap file the directory (relative to this one) ../Pcaps/wombat-test.pcap. We may script the execution of the tests as below:

./run.py ../Pcaps/wombat-tests.pcap  wombat
./plot.py --simulation "plots/wombat"

The command ./plot.py --paper will also create some aditional plots for the paper. To recreate all the plots on the paper, after creating the enviroment, we must execute:

./run.py Pcaps/skype.pcap skype
./run.py Pcaps/bigFlows.pcap bigFlows
./run.py Pcaps/equinix-1s.pcap equinix-1s
./run.py Pcaps/lanDiurnal.pcap lanDiurnal
./plots.py --simulation "./plots/skype/"
./plot.py --simulation "./plots/bigFlows/"
./plot.py --simulation "./plots/equinix-1s/"
./plot.py --simulation "./plots/lanDiurnal/"
./plot.py --paper

These are the set of scripts located at the dataProcessor directory, used by the run.py script.

• pcap-filter.sh : extract inter packet times from pcaps.
  ├── timerelative2timedelta.m: script used by pcap filter
• dataProcessor.m: run simulations and stores the data on data/ directory.
  ├── adiff.m: calc the absolute difference
  ├── cdfCauchyPlot.m: create the values of a Cauchy CDF distribution, and plot in a figure
  ├── cdfExponentialPlot.m: create the values of a Exponential CDF distribution, and plot in a figure
  ├── cdfNormalPlot.m: create the values of a Normal CDF distribution, and plot in a figure
  ├── cdfWeibullPlot.m: create the values of a Weibull CDF distribution, and plot in a figure
  ├── cdfParetoPlot.m: create the values of a Pareto CDF distribution, and plot in a figure
  ├── cdfplot.m: create the values of a Cauchy CDF distribution, and plot in a figure
  ├── computeCost.m: compute cost for linear regression
  ├── cumulativeData.m: acumulates a vector
  ├── gradientDescent.m: gradient descendent algorithm
  ├── informationCriterion.m: calcs AIC or BIC of a given function and dataset
  ├── likehood_log.m: calcs the logarithm of the likelihood function
  ├── matrix2File.m: save matrix into a text file
  ├── empiricalCdf.m: eval empirical CDF
  ├── plotData.m: wrapper for plot x and y data
  ├── qqPlot.m: wrapper for qqplots on octave wraper
  ├── sameLength.m: ensure two vecters have the same size. If not, the bigget is truncated
  ├── setxlabels.m: set x tick labels on axis on figures
  ├── sff2File.m: vector to file
  ├── data/: place where dataProcessor.m saves the generated data
  ├── figures/: figures plotted by dataProcessor
• calcCostFunction.py: aux script, this script calcs the cost function for the simulated data and saves in the file costFunction.dat.
• aicBicRelativeDiff.py: script to calc the relative difference between AIC and BIC.