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Machine learning based web application firewall

Machine learning based Web Application firewall for detection attacks such as SQL injections, XSS and shell script injections.

Course project for TDA602 Contributors: Filip Granqvist & Oskar Holmberg

Requirements:

  • Python 3.x
  • Jupyter notebook
  • Python libraries
    • Pandas
    • Numpy
    • Sklearn
    • matplotlib
    • seaborn
    • scipy
  • Node.js (to run demo server)

Follow along our development process in these notebooks:

  • 0_Data_wrangling.ipynb (Formating other sources of payload datasets into a common format (don't step through this))
  • 1_Data_cleaning.ipynb (Cleaning the data and output into a common .csv file)
  • 2_Data_analysis.ipynb (All analysis, training, evaluation and saving models to pickles (not recommended to step through the training section, takes a long time))

To run notebooks (they can also be read from github):

  1. Install jupyter notebook
  2. type in cmd: jupyter notebook <notebookfile.ipynb>
  3. step through each part of the notebook using Ctrl+Enter or from the toolbar

Plots_technical_background.ipynb contains junk used to create images for the report

Demo-server contains a Node.js server with our best classifier (in the form of a .pickle) implemented available for live testing
See README.md in demo-server for instructions on how to set up the server

images contains images used for the report

data folder contains our malicious and non-malicious data. Also contains trained classifiers in form of .pickle files
tfidf_2grams_randomforest.p contains our single best classifier
trained_classifiers.p contains all our classifiers along with performance metrics. But this is not the final version, it was too big for github. Download the final version from here: https://1drv.ms/f/s!Aj1zBHCOJiQFgbQwDswBYtpzB1Pulg and replace the old one