Skip to content
No description or website provided.
Branch: master
Clone or download
Latest commit 5f660d9 Feb 26, 2018
Permalink
Type Name Latest commit message Commit time
Failed to load latest commit information.
data Create README.md May 29, 2017
demo-server
images
.gitignore
0_Data_wrangling.ipynb
1_Data_cleaning.ipynb comments and cleaning May 15, 2017
2_Data_analysis.ipynb
LICENSE
Plots_technical_background.ipynb
README.md
report.pdf report Aug 8, 2017

README.md

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

You can’t perform that action at this time.