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.
It proposed two intrusion detection systems by implementing many machine learning algorithms, including tree-based algorithms (decision tree, random forest, XGBoost, etc.), unsupervised learning algorithms (k-means), ensemble learning algorithms (stacking), and hyperparameter optimization techniques (Bayesian optimization)**.
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Another intrusion detection system development code using convolutional neural networks (CNNs) and transfer learning techniques can be found in: Intrusion-Detection-System-Using-CNN-and-Transfer-Learning
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A comprehensive hyperparameter optimization tutorial code can be found in: Hyperparameter-Optimization-of-Machine-Learning-Algorithms
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.
CAN-intrusion dataset, a benchmark network security dataset for intra-vehicle intrusion detection
- Publicly available at: https://ocslab.hksecurity.net/Datasets/CAN-intrusion-dataset
- Can be processed using the same 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"
- Decision tree (DT)
- Random forest (RF)
- Extra trees (ET)
- XGBoost
- Stacking
- K-means
- 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
- Python 3
- scikit-learn
- Xgboost
- FCBF
- scikit-optimize
- hyperopt
Please feel free to contact us for any questions or cooperation opportunities. We will be happy to help.
- Email: [email protected]
- GitHub: LiYangHart and Western OC2 Lab
- LinkedIn: Li Yang
- Google Scholar: Li Yang and OC2 Lab
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{9443234,
author={Yang, Li and Moubayed, Abdallah and Shami, Abdallah},
journal={IEEE Internet of Things Journal},
title={MTH-IDS: A Multi-Tiered Hybrid Intrusion Detection System for Internet of Vehicles},
year={2021},
pages={1-17},
doi={10.1109/JIOT.2021.3084796}}