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Code for IDS-ML: intrusion detection system development using machine learning algorithms (Decision tree, random forest, extra trees, XGBoost, stacking, k-means, Bayesian optimization..)
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# Intrusion-Detection-System-Using-Machine-Learning This repository contains the code for the project "IDS-ML: Intrusion Detection System Development Using Machine Learning". The code and proposed Intrusion Detection System (IDSs) are general models that can be used in any IDS and anomaly detection applications. In this project, three papers have been published: * L. Yang, A. Moubayed, I. Hamieh and A. Shami, "[Tree-Based Intelligent Intrusion Detection System in Internet of Vehicles](https://arxiv.org/pdf/1910.08635.pdf)," in 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](https://arxiv.org/pdf/2105.13289.pdf),” IEEE Internet of Things Journal, vol. 9, no. 1, pp. 616-632, Jan.1, 2022, doi: 10.1109/JIOT.2021.3084796. * L. Yang, A. Shami, G. Stevens, and S. DeRusett, “[LCCDE: A Decision-Based Ensemble Framework for Intrusion Detection in The Internet of Vehicles](https://arxiv.org/pdf/2208.03399.pdf)," in 2022 IEEE Global Communications Conference (GLOBECOM), 2022, pp. 1-6, doi: 10.1109/GLOBECOM48099.2022.10001280. The code introduction of this repository is publicly available at: * L. Yang, and A. Shami, “[IDS-ML: An open source code for Intrusion Detection System development using Machine Learning](https://www.sciencedirect.com/science/article/pii/S2665963822001300)," Software Impacts, vol. 14, pp. 1-4, 2022, doi: 10.1016/j.simpa.2022.100446. This repository proposed three **intrusion detection systems** by implementing many **machine learning** algorithms, including tree-based algorithms (**decision tree, random forest, XGBoost, LightGBM, CatBoost etc.**), unsupervised learning algorithms (**k-means**), ensemble learning algorithms (**stacking, proposed LCCDE**), and hyperparameter optimization techniques (**Bayesian optimization**)**. - 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](https://github.com/Western-OC2-Lab/Intrusion-Detection-System-Using-CNN-and-Transfer-Learning) - A comprehensive **hyperparameter optimization** tutorial code can be found in: [Hyperparameter-Optimization-of-Machine-Learning-Algorithms](https://github.com/LiYangHart/Hyperparameter-Optimization-of-Machine-Learning-Algorithms) ## Paper Abstract ### Paper 1: Tree-Based Intelligent Intrusion Detection System in Internet of Vehicles   The use of autonomous vehicles (AVs) is a promising technology in Intelligent Transportation Systems (ITSs) to improve safety and driving efficiency. Vehicle-to-everything (V2X) technology enables communication among vehicles and other infrastructures. However, AVs and Internet of Vehicles (IoV) are vulnerable to different types of cyber-attacks such as denial of service, spoofing, and sniffing attacks. An intelligent IDS is proposed in this paper for network attack detection that can be applied to not only Controller Area Network (CAN) bus of AVs but also on general IoVs. The proposed IDS utilizes tree-based ML algorithms including decision tree (DT), random forest (RF), extra trees (ET), and Extreme Gradient Boosting (XGBoost). The results from the implementation of the proposed intrusion detection system on standard data sets indicate that the system has the ability to identify various cyber-attacks in the AV networks. Furthermore, the proposed ensemble learning and feature selection approaches enable the proposed system to achieve high detection rate and low computational cost simultaneously. **<p align="center">Figure 1: The overview of the tree-based IDS model.</p>** <p align="center"> <img src="https://github.com/Western-OC2-Lab/Intrusion-Detection-System-Using-Machine-Learning/blob/main/Figures/Tree-based_IDS_Overview.jpg" width="280" /> </
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Li Yang, PhD · Ontario Tech University · Canada
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:f51eccbefde6c816, llm:topics: intrusion-detection, intrusion-detection-system, cyber-security, network-security, machine-learning; description/readme: IDS development using ML algorithms (decision tree, random forest, XGBoost, LightGBM, CatBoost, k-means, stacking, Bayesian optimization); datasets referenced (CICIDS2017)
matched fp:f51eccbefde6c816, llm:topics: intrusion-detection, intrusion-detection-system, cyber-security, network-security, machine-learning; description/readme: IDS development using ML algorithms (decision tree, random forest, XGBoost, LightGBM, CatBoost, k-means, stacking, Bayesian optimization); datasets referenced (CICIDS2017)