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Cyberattack-Detection

Author: Antoine DELPLACE
Last update: 17/01/2020

This repository corresponds to the source code used for the Advanced Security project "Cyber Attack Detection thanks to Machine Learning Algorithms". This work has been carried out by Antoine Delplace, Sheryl Hermoso and Kristofer Anandita.

Method description

The aim of the project is to find a mechanism that can detect cyber attacks by analysing flows in a network. To do this, a benchmark of different machine learning methods is performed on a large netflow dataset (see Report).

Usage

Dependencies

  • Python 3.6.8
  • Numpy 1.16.2
  • Pandas 0.24.2
  • Scipy 1.2.1
  • Datetime 4.3
  • h5py 2.9.0
  • Matplotlib 3.1.1
  • Scikit-learn 0.20.3
  • Tensorflow 1.14 -- predict_neural_network_stat_analysis.py

File description

  1. preprocessing1.py and preprocessing2.py are the files used to extract meaningful data from the raw netflow files.

  2. feature_extraction.py and pca_tsne.py try to decrease the number of features using embedded methods or dimensionality reduction techniques.

  3. predict_random_forest_bootstrap.py and predict_svm.py implement a classifier to detect malware with Random Forest and with Support Vector Machine.

  4. predict_gradient_boosting_stat_analysis.py, predict_logistic_reg_stat_analysis.py, predict_neural_network_stat_analysis.py and predict_statistic_analysis_bootstrap.py carry out statistical analysis of different classifiers: Gradient Boosting, Logistic Regression, Neural Network and Random Forest with bootstrap respectively.

Results

The experiments show that Random Forest can detect more than 95% of botnets for 8 out of 13 scenarios. Moreover, the accuracy on the 5 most difficult scenarios can be increased thanks to a bootstrap method. For more details, see the report.

References

  1. A. Delplace, S. Hermoso and K. Anandita. "Cyber Attack Detection thanks to Machine Learning Algorithms", Advanced Security Report at the University of Queensland, May 2019. arXiv:2001.06309