Tree-based Intelligent Intrusion Detection System in Internet of Vehicles
Abstract
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. In this paper, an intelligent intrusion detection system (IDS) is proposed based on tree-structure machine learning models. 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.
Keywords
Cite
@article{arxiv.1910.08635,
title = {Tree-based Intelligent Intrusion Detection System in Internet of Vehicles},
author = {Li Yang and Abdallah Moubayed and Ismail Hamieh and Abdallah Shami},
journal= {arXiv preprint arXiv:1910.08635},
year = {2022}
}
Comments
Published in IEEE Global Communications Conference (GLOBECOM) 2019; Code is available at Github link: https://github.com/Western-OC2-Lab/Intrusion-Detection-System-Using-Machine-Learning