Intrusion-Detection-System-Using-Machine-Learning

This repository contains the code for the project "Intrusion Detection System Development for Autonomous/Connected Vehicles". In this project, two papers have been published:

• L. Yang, A. Moubayed, I. Hamieh and A. Shami, "Tree-Based Intelligent Intrusion Detection System in Internet of Vehicles," 2019 IEEE Global Communications Conference (GLOBECOM), 2019, pp. 1-6, doi: 10.1109/GLOBECOM38437.2019.9013892.
• L. Yang, A. Moubayed, and A. Shami, “MTH-IDS: A Multi-Tiered Hybrid Intrusion Detection System for Internet of Vehicles,” IEEE Internet of Things Journal, 2021, doi: 10.1109/JIOT.2021.3084796.

Implementation

Dataset

CICIDS2017 dataset, a popular network traffic dataset for intrusion detection problems

• Publicly available at: https://www.unb.ca/cic/datasets/ids-2017.html

For the purpose of displaying the experimental results in Jupyter Notebook, the sampled subsets of CICIDS2017 is used in the sample code. The subsets are in the "data" folder.

Code

• Tree-based_IDS_GlobeCom19.ipynb: code for the paper "Tree-Based Intelligent Intrusion Detection System in Internet of Vehicles"
• MTH_IDS_IoTJ.ipynb: code for the paper "MTH-IDS: A Multi-Tiered Hybrid Intrusion Detection System for Internet of Vehicles"

Machine Learning Algorithms

• Decision tree (DT)
• Random forest (RF)
• Extra trees (ET)
• XGBoost
• Stacking
• K-means

Hyperparameter Optimization Methods

• Bayesian Optimization with Gaussian Processes (BO-GP)
• Bayesian Optimization with Tree-structured Parzen Estimator (BO-TPE)

If you are interested in hyperparameter tuning of machine learning algorithms, please see the code in the following link:
https://github.com/LiYangHart/Hyperparameter-Optimization-of-Machine-Learning-Algorithms

Requirements & Libraries

• Python 3.5
• scikit-learn
• Xgboost
• FCBF
• scikit-optimize
• hyperopt

Contact-Info

Please feel free to contact us for any questions or cooperation opportunities. We will be happy to help.

• Email: liyanghart@gmail.com
• GitHub: LiYangHart and Western OC2 Lab
• LinkedIn: Li Yang
• Google Scholar: Li Yang and OC2 Lab

Citation

If you find this repository useful in your research, please cite one of the following two articles as:

L. Yang, A. Moubayed, I. Hamieh and A. Shami, "Tree-Based Intelligent Intrusion Detection System in Internet of Vehicles," 2019 IEEE Global Communications Conference (GLOBECOM), 2019, pp. 1-6, doi: 10.1109/GLOBECOM38437.2019.9013892.

@INPROCEEDINGS{9013892,
  author={Yang, Li and Moubayed, Abdallah and Hamieh, Ismail and Shami, Abdallah},
  booktitle={2019 IEEE Global Communications Conference (GLOBECOM)}, 
  title={Tree-Based Intelligent Intrusion Detection System in Internet of Vehicles}, 
  year={2019},
  pages={1-6},
  doi={10.1109/GLOBECOM38437.2019.9013892}
  }

L. Yang, A. Moubayed, and A. Shami, “MTH-IDS: A Multi-Tiered Hybrid Intrusion Detection System for Internet of Vehicles,” IEEE Internet of Things Journal, 2021, doi: 10.1109/JIOT.2021.3084796.

@article{YANG20210525,
title = "MTH-IDS: A Multi-Tiered Hybrid Intrusion Detection System for Internet of Vehicles",
author = "Yang, Li and Moubayed, Abdallah and Shami, Abdallah",
journal = "IEEE Internet of Things Journal",
year = "2021",
doi={10.1109/JIOT.2021.3084796}
}