Code for intrusion detection system (IDS) development using CNN models and transfer learning
This is the code for the paper entitled "A Transfer Learning and Optimized CNN Based Intrusion Detection System for Internet of Vehicles" published in IEEE International Conference on Communications (IEEE ICC), doi: 10.1109/ICC45855.2022.9838780.
This repository introduces how to use convolutional neural networks (CNNs) and transfer learning techniques to develop intrusion detection systems. Ensemble learning and hyperparameter optimization techniques are also used to achieve optimized model performance.
Another intrusion detection system development code using decision tree-based machine learning algorithms (Decision tree, random forest, XGBoost, stacking, etc.) can be found in: Intrusion-Detection-System-Using-Machine-Learning
A comprehensive hyperparameter optimization tutorial code can be found in: Hyperparameter-Optimization-of-Machine-Learning-Algorithms
Modern vehicles, including autonomous vehicles and connected vehicles, are increasingly connected to the external world, which enables various functionalities and services. However, the improving connectivity also increases the attack surfaces of the Internet of Vehicles (IoV), causing its vulnerabilities to cyber-threats. Due to the lack of authentication and encryption procedures in vehicular networks, Intrusion Detection Systems (IDSs) are essential approaches to protect modern vehicle systems from network attacks. In this paper, a transfer learning and ensemble learning-based IDS is proposed for IoV systems using convolutional neural networks (CNNs) and hyper-parameter optimization techniques. In the experiments, the proposed IDS has demonstrated over 99.25% detection rates and F1-scores on two well-known public benchmark IoV security datasets: the Car-Hacking dataset and the CICIDS2017 dataset. This shows the effectiveness of the proposed IDS for cyber-attack detection in both intra-vehicle and external vehicular networks.
For the purpose of displaying the experimental results in Jupyter Notebook, the sampled subset of the CAN-intrusion dataset is used in the sample code. The subsets are in the "data" folder.
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If you find this repository useful in your research, please cite this article as:
L. Yang and A. Shami, "A Transfer Learning and Optimized CNN Based Intrusion Detection System for Internet of Vehicles," ICC 2022 - IEEE International Conference on Communications, 2022, pp. 2774-2779, doi: 10.1109/ICC45855.2022.9838780.
@INPROCEEDINGS{9838780,
author={Yang, Li and Shami, Abdallah},
booktitle={ICC 2022 - IEEE International Conference on Communications},
title={A Transfer Learning and Optimized CNN Based Intrusion Detection System for Internet of Vehicles},
year={2022},
pages={2774-2779},
doi={10.1109/ICC45855.2022.9838780}}