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".

• The master branch can only run on Windows environment.
• The ubuntu branch can run on Ubuntu Linux 16.04 LTS environment.

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

   • The requried packages are listed as follow:
     • numpy 1.16.4
     • PIL 1.1.6

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!

• Author
  • Wei Wang - [email protected]
• Contributor
  • David Lu

License

Mozilla Public License Version 2.0