USTC-TK2016

This repository is a toolkit called "USTC-TK2016", which is used to parse network traffic (.pcap file). Besides, the dataset is "USTC-TFC2016".

NOTICE: This repository credits to echowei/DeepTraffic

Installation

  1. Clone this repository on your machine 
    # Clone the repository on "master" branch
$ git clone -b master https://github.com/yungshenglu/USTC-TK2016
  2. Install the required packages via the following command 
    # Run the command at the root of the repository
$ pip3 install -r requirements.txt

Execution

NOTICE: You are on the master branch now!

  1. Download the traffic dataset USTC-TFC2016 and put it into the directory 1_Pcap\
    • You can download the traffic dataset USTC-TFC2016 from my another repository.
  2. Open the PowerShell and run 1_Pcap2Session.ps1 (take a few minutes)
    • To split the PCAP file by each session, please make sure the line 10 and 14 in 1_Pcap2Session.ps1 is uncommented and make line 11 and 15 is in comment.
    • To split the PCAp file by each flow, please make sure the line 11 and 15 in 1_Pcap2Session.ps1 is uncommented and make line 10 and 14 is in comment.
    • Run 1_Pcap2Session.ps1 
      # Make sure your current directory is correct
PS> .\1_Pcap2Session.ps1
    • If succeed, you will see the following files (folders) in folder 2_Session\
      • AllLayers\
      • L7\
  3. Run 2_ProcessSession.ps1 (take a few minutes) 
    # Make sure your current directory is correct
PS> .\2_ProcessSession.ps1
    • If succeed, you will see the following files (folders) in folder 3_ProcessedSession\
      • FilteredSession\ - Get the top 60000 large PCAP files
      • TrimedSession\ - Trim the filtered PCAP files into size 784 bytes (28 x 28) and append 0x00 if the PCAP file is shorter than 784 bytes
      • The files in subdirectory Test\ and Train\ is random picked from dataset.
  4. Run 3_Session2Png.py (take a few minutes) 
    # Make sure your current directory is correct
PS> python3 3_Session2png.py
    • If succeed, you will see the following files (folders) in folder 4_Png\
      • Test\ - For testing
      • Train\ - For training
  5. Run 4_Png2Mnist.py (take a few minutes) 
    # Make sure your current directory is correct
PS> python3 4_Png2Mnist.py
    • If succeed, you will see the the training datasets in folder 5_Mnist\
      • train-images-idx1-ubyte
      • train-images-idx3-ubyte
      • train-images-idx1-ubyte.gz
      • train-images-idx3-ubyte.gz

Contributor

NOTICE: You can follow the contributing process CONTRIBUTING.md to join me. I am very welcome any issue!

License

Mozilla Public License Version 2.0