Correcting Datasets - Deep Learning (SBRC21)

Algorithm for correcting sessions of users of large-scale peer-to-peer systems based on deep learning.

Complementary repository to the work available at: https://sol.sbc.org.br/index.php/sbrc/article/view/16766/16608

Input parameters:

Torrent Trace Correct - Machine Learning

Arguments(run_SBRC21.py):
    
    -h, --help              |   Show this help message and exit
    --output                |   Full name of the output file with analysis results (default=sbrc21.txt)
    --append                |   Append output logging file with analysis results (default=False)
    --trials                |   Number of trials (default=1)
    --start_trials          |   Start trials (default=0)
    --skip_train            |   Skip training of the machine learning model
    --campaign              |   Campaign [demo, sbrc21] (default=demo)
    --verbosity             |   Verbosity logging level (INFO=20 DEBUG=10)

--------------------------------------------------------------

Arguments(main.py):

    -h,--help                 |   Show this help message and exit
    --original_swarm_file     |   File of ground truth.
    --training_swarm_file     |   File of training samples
    --corrected_swarm_file    |   File of correction
    --failed_swarm_file       |   File of failed swarm
    --analyse_file            |   Analyse results with statistics
    --analyse_file_mode       |   Open mode (e.g. 'w' or 'a')
    --model_architecture_file |   Full model architecture file
    --model_weights_file      |   Full model weights file
    --num_epochs              |   Number of epochs
    --threshold               |   i.e. alpha (e.g. 0.5 - 0.95)
    --dense_layers            |   Number of dense layers (e.g. 1, 2, 3)
    --pif PIF                 |   pif (only for statistics)
    --dataset DATASET         |   Dataset (only for statistics)
    --seed SEED               |   Seed (only for statistics)
    --skip_train, -t          |   Skip training of the machine learning model
    --skip_correct, -c        |   Skip correction of the dataset
    --skip_analyse, -a        |   Skip analyzis of the results
    --verbosity VERBOSITY, -v |   Verbosity logging level (INFO=20 DEBUG=10)


    --------------------------------------------------------------
    Full traces available at: https://github.com/ComputerNetworks-UFRGS/TraceCollection/tree/master/01_traces

Run (all experiments):

python3 run_sbrc21.py -c sbrc

Run (only one scenario)

python3 main.py

Requirements:

matplotlib 3.4.1 tensorflow 2.4.1 tqdm 4.60.0 numpy 1.18.5

keras 2.4.3 setuptools 45.2.0 h5py 2.10.0